⽂献综述：基于贝叶斯⽹络的新能源汽车电⼒驱动系统的动态故障风险预测与维护
Literature Review: Dynamic Fault Risk Prediction and Maintenance of New Energy Vehicle Electric Drive Systems Based on Bayesian Networks

摘要
Abstract

随着新能源汽车 (NEV)技术的⻜速发展 ，其电⼒驱动系统 (EDS) 的可靠性与安全性⽇益受到关
With the rapid development of new energy vehicle (NEV) technology, the reliability and safety of its electric drive system (EDS) have become increasingly important

注。 电⼒驱动系统作为新能源汽车的核⼼ ，其复杂的结构和动态多变的⼯作环境对故障预测和维 护策略提出了严峻挑战。传统的可靠性分析⽅法在处理动态不确定性、 多源信息融合以及复杂耦 合故障⽅⾯存在局限性。 贝叶斯⽹络 (BN)作为⼀种强⼤的概率图模型 ，因其在不确定性推理、知 识表达和数据融合⽅⾯的独特优势 ，在复杂⼯程系统的故障诊断与预测领域展现出巨⼤潜⼒ 。本 综述旨在系统梳理和评述基于贝叶斯⽹络及其扩展模型（如动态贝叶斯⽹络 DBN、 贝叶斯神经⽹ 络 BNN） 在新能源汽车电⼒驱动系统动态故障⻛险预测与维护决策⽅⾯的研究进展。 ⾸先 ，概述 了新能源汽车电⼒驱动系统的基本构成、 主要故障模式及其对可靠性的核⼼挑战。 其次 ，探讨了 传统可靠性分析⽅法（如FMEA、 FTA） 应⽤于电⼒驱动系统的局限性 ，并阐述了贝叶斯⽹络的基 本理论及其在可靠性⼯程中的应⽤基础 ，特别是在处理多态系统和模糊不确定性⽅⾯的能⼒ 。在 此基础上 ，综述重点分析了基于贝叶斯⽹络的电⼒驱动系统动态故障⻛险预测技术 ，包括利⽤动 态贝叶斯⽹络处理时间序列数据、从运⾏数据中提取关键特征 ， 以及在电机、控制器（含IGBT模 块） 、轴承与齿轮等关键部件故障预测中的应⽤案例。进⼀步 ，本综述探讨了如何利⽤贝叶斯⽹ 络的预测结果（如剩余使⽤寿命RUL） 优化视情维护 (CBM)和预测性维护 (PdM)策略 ，并结合成 本效益分析进⾏维护决策。 此外 ，还讨论了贝叶斯神经⽹络 (BNN)和物理信息神经⽹络 (PINN)
Note. As the core of new energy vehicles, electric drive systems present significant challenges for fault prediction and maintenance strategies due to their complex structure and dynamic operating environments. Traditional reliability analysis methods exhibit limitations in handling dynamic uncertainties, multi-source information fusion, and complex coupled faults. Bayesian Networks (BN), as a powerful probabilistic graphical model, demonstrate substantial potential in fault diagnosis and prediction for complex engineering systems owing to their unique advantages in uncertainty reasoning, knowledge representation, and data fusion. This review systematically examines and evaluates research progress in dynamic fault risk prediction and maintenance decision-making for new energy vehicle electric drive systems based on Bayesian Networks and their extended models (e.g., Dynamic Bayesian Networks DBN, Bayesian Neural Networks BNN). First, it outlines the fundamental components of electric drive systems in new energy vehicles, primary failure modes, and core reliability challenges. Second, it discusses the limitations of traditional reliability analysis methods (e.g., FMEA, FTA) when applied to electric drive systems and elaborates on the basic theory of Bayesian Networks and their foundational applications in reliability engineering, particularly their capability in handling multi-state systems and fuzzy uncertainties. Building on this, the review focuses on analyzing Bayesian Network-based dynamic fault risk prediction technologies for electric drive systems, including the use of Dynamic Bayesian Networks for processing time-series data, extracting key features from operational data, and application cases in fault prediction for critical components such as motors, controllers (including IGBT modules), bearings, and gears. Furthermore, the review explores how to utilize Bayesian Network prediction results (e.g., Remaining Useful Life RUL) to optimize Condition-Based Maintenance (CBM) and Predictive Maintenance (PdM) strategies, integrating cost-benefit analysis for maintenance decision-making. Additionally, it discusses Bayesian Neural Networks (BNN) and Physics-Informed Neural Networks (PINN).

等⾼级贝叶斯⽅法在提升预测精度和模型鲁棒性⽅⾯的潜⼒ 。最后 ，总结了当前研究⾯临的主要 挑战 ，如数据获取与质量、模型复杂性与计算效率、模型泛化能⼒以及标准化等问题 ，并展望了 未来的研究⽅向 ，包括多物理场耦合建模、模型可解释性增强以及与数字孪⽣等技术的深度融
Advanced Bayesian methods demonstrate significant potential in enhancing prediction accuracy and model robustness. Finally, the paper summarizes the main challenges in current research, including data acquisition and quality, model complexity and computational efficiency, model generalization capability, and standardization issues. It also outlines future research directions, such as multi-physics coupling modeling, enhanced model interpretability, and deep integration with technologies like digital twins.

合。本综述期望为新能源汽车电⼒驱动系统的智能化故障管理提供理论参考和技术借鉴。
This review aims to provide theoretical references and technical guidance for intelligent fault management in new energy vehicle electric drive systems.

关键词
Keywords

新能源汽车； 电⼒驱动系统； 贝叶斯⽹络； 动态故障预测；预测性维护； 可靠性；剩余使⽤寿命
New energy vehicles; Electric drive systems; Bayesian networks; Dynamic fault prediction; Predictive maintenance; Reliability; Remaining useful life

1. 引⾔
1. Introduction

1.1. 新能源汽车电⼒驱动系统概述
1.1. Overview of Electric Drive Systems for New Energy Vehicles

新能源汽车 (New Energy Vehicles, NEVs) 作为应对能源危机和环境污染的重要战略⽅向 , 其核⼼技术之⼀便是电⼒驱动系统 (Electric Drive System, EDS)。 电⼒驱动系统不仅是 车辆的动⼒来源 ，也直接关系到整车的能源效率、驾驶性能和运⾏安全。
New Energy Vehicles (NEVs), as a crucial strategic direction for addressing energy crises and environmental pollution, rely on Electric Drive Systems (EDS) as one of their core technologies. The electric drive system not only serves as the vehicle's power source but also directly impacts the overall energy efficiency, driving performance, and operational safety of the vehicle.

● 1.1.1. 核⼼组成与功能
● 1.1.1. Core Components and Functions

新能源汽车电⼒驱动系统通常是⼀个⾼度集成的复杂系统 ，主要由驱动电机 (Drive
A Review of Bayesian Network-Driven System Predictive Maintenance The electric drive system of new energy vehicles is typically a highly integrated complex system, primarily consisting of the drive motor (Drive..

Motor, DM)、 电机控制器 (Motor Controller Unit, MCU)、动⼒电池系统、车载充电 系统以及⾼压配电单元 (Power Distribution Unit, PDU) 等组成 1。其中 ，驱动电机负 责将电能转化为机械能 ，驱动车辆⾏驶； 电机控制器则根据驾驶员的意图和车辆状态 , 精确控制电机的转速和转矩 ，其核⼼部件如绝缘栅双极型晶体管 (IGBT)模块对系 统性能⾄关重要 1。此外 ， 电⼒驱动系统还包括电源模块、驱动保护模块和信号模块 等辅助单元 ，它们共同确保系统的稳定⾼效运⾏ 1。相较于传统内燃机汽车 ，新能源
Motor (DM), Motor Controller Unit (MCU), power battery system, onboard charging system, and Power Distribution Unit (PDU) [1]. Among these, the drive motor is responsible for converting electrical energy into mechanical energy to propel the vehicle; the motor controller precisely regulates the motor's speed and torque based on driver input and vehicle status, with core components such as Insulated Gate Bipolar Transistor (IGBT) modules being critical to system performance [1]. Additionally, the electric drive system includes auxiliary units such as power modules, drive protection modules, and signal modules, which collectively ensure stable and efficient system operation [1]. Compared to traditional internal combustion engine vehicles, new energy...

汽⻋的电⼒驱动系统具有⾼启动转矩、宽调速范围和⾼功率密度等显著特点 ，通常集 成了电机、减速器和控制器， 以实现能量的⾼效转换和回收 1。
The electric drive system of automobiles exhibits notable characteristics such as high starting torque, wide speed regulation range, and high power density. It typically integrates motors, reducers, and controllers to achieve efficient energy conversion and recovery 1.

● 1.1.2. 可靠性⾯临的挑战与重要性
● 1.1.2. Challenges and Importance of Reliability

随着新能源汽⻋电机扭矩和功率的显著提升 ， 电⼒驱动系统⾯临的失效风险也随之加 剧 1。其⼯作环境复杂多变 ，常处于⾼低温、振动、湿热等交变应⼒之下 ，同时承受 着⾼强度、动态变化的负载 ，这些因素共同构成了电⼒驱动系统可靠性的严峻挑战 1 。 电⼒驱动系统的可靠性不仅直接影响⻋辆的动⼒性、经济性和安全性 ，更是决定了 新能源汽⻋的市场竞争⼒与⽤⼾的接受程度 1。然⽽ ，现有的针对传统汽⻋的可靠性 测试规范往往难以有效覆盖新能源汽⻋电⼒驱动系统的实际服役载荷强度 ，⽽已有的 电⼒驱动系统可靠性标准在整⻋全⽣命周期损伤⽬标的准确定义、与⽤⼾实际使⽤⼯ 况的关联性以及极端⼯况评估等⽅⾯仍存在不⾜ ，甚⾄在不同部件间的失效模式上也 缺乏⼀致性 ，这些都凸显了提升电⼒驱动系统可靠性研究的迫切性与重要性 1。 电⼒ 驱动系统各部件之间复杂的耦合失效机制 ，例如IGBT模块的散热问题可能加速电机 绕组绝缘⽼化 ，也对传统的、侧重于单⼀组件的可靠性评估⽅法提出了挑战。
With the significant increase in torque and power of new energy vehicle motors, the failure risks faced by electric drive systems have also intensified [1]. These systems operate in complex and variable environments, often subjected to alternating stresses such as high and low temperatures, vibrations, and humidity, while simultaneously bearing high-intensity, dynamically changing loads. These factors collectively pose severe challenges to the reliability of electric drive systems [1]. The reliability of electric drive systems not only directly affects the vehicle's power performance, fuel economy, and safety but also determines the market competitiveness of new energy vehicles and user acceptance [1]. However, existing reliability testing standards for traditional vehicles often fail to effectively cover the actual service load intensity of new energy vehicle electric drive systems. Moreover, current reliability standards for electric drive systems still have shortcomings in accurately defining full-lifecycle damage targets, correlating with actual user operating conditions, and evaluating extreme scenarios. There is even a lack of consistency in failure modes across different components, highlighting the urgency and importance of enhancing research on electric drive system reliability [1]. The complex coupling failure mechanisms among various components of electric drive systems—such as the heat dissipation issues of IGBT modules potentially accelerating the insulation aging of motor windings—also challenge traditional reliability assessment methods that focus on single components.

1.2. 动态故障风险预测与维护的需求
1.2. The Need for Dynamic Fault Risk Prediction and Maintenance

传统基于固定周期或事后修复的维护策略 ，已难以满⾜新能源汽⻋电⼒驱动系统对⾼可靠 性和低运营成本的要求。 电⼒驱动系统在实际运⾏中 ，其健康状态是动态变化的 ，故障的 发⽣往往是⼀个渐进的累积过程。 因此 ，迫切需要发展动态故障风险预测技术 ，以便在故 障发⽣的早期阶段及时预警 ，并制定视情维护 (Condition-Based Maintenance, CBM) 或 预测性维护 (Predictive Maintenance, PdM) 策略 2。这种转变不仅能够显著降低因突发故 障导致的维修成本和停运损失 ，更能有效提升⾏⻋安全。实时采集和分析⻋辆运⾏数据 ， 如电流、 电压、温度、振动、转速和扭矩等 ，是实现动态风险评估和主动维护决策的关键 1。这种从传统静态可靠性评估向动态、数据驱动的风险管理模式的转变 ，不仅是技术上 的进步 ，更是符合⼯业4.0时代发展趋势的必然要求 ， 旨在通过智能化⼿段提升新能源汽 ⻋的整体运营效能和安全保障⽔平。
Traditional maintenance strategies based on fixed intervals or post-failure repairs can no longer meet the high reliability and low operational cost requirements of new energy vehicle electric drive systems. During actual operation, the health status of electric drive systems changes dynamically, and faults often develop through a gradual accumulation process. Therefore, there is an urgent need to develop dynamic fault risk prediction technologies to provide early warnings during the initial stages of fault development and to formulate condition-based maintenance (CBM) or predictive maintenance (PdM) strategies [2]. This shift not only significantly reduces repair costs and downtime losses caused by sudden failures but also effectively enhances driving safety. Real-time collection and analysis of vehicle operating data—such as current, voltage, temperature, vibration, speed, and torque—are key to achieving dynamic risk assessment and proactive maintenance decisions [1]. This transition from traditional static reliability assessment to dynamic, data-driven risk management models represents not only a technological advancement but also an inevitable requirement aligned with the development trends of Industry 4.0, aiming to enhance the overall operational efficiency and safety assurance of new energy vehicles through intelligent means.

1.3. 贝叶斯⽹络在复杂系统可靠性分析中的潜⼒
1.3. The Potential of Bayesian Networks in Reliability Analysis of Complex Systems

贝叶斯⽹络 (Bayesian Network, BN) 作为⼀种基于概率论的图形化模型 ，为复杂系统的 不确定性建模和推理提供了强⼤的⼯具 1。 它能够有效地融合来⾃不同来源的信息 ，如历 史数据、传感器监测信息以及领域专家的经验知识 ，从⽽在信息不完备或存在不确定性的 情况下进⾏逻辑推理和预测 1。贝叶斯⽹络在故障诊断（从现象推断原因）和故障预测
Bayesian Network (BN), as a probabilistic graphical model, provides a powerful tool for uncertainty modeling and reasoning in complex systems [1]. It can effectively integrate information from diverse sources, such as historical data, sensor monitoring information, and domain expert knowledge, thereby enabling logical reasoning and prediction under conditions of incomplete information or uncertainty [1]. The bidirectional reasoning capability of Bayesian Networks in fault diagnosis (from symptoms to causes) and fault prediction

（从当前状态推断未来趋势） ⽅⾯的双向推理能⼒ ，使其⾮常适合应⽤于电⼒驱动系统的 动态故障风险评估和剩余使⽤寿命 (Remaining Useful Life, RUL) 预测 ，进⽽为制定优化 的维护策略提供决策⽀持 2。
(from current states to future trends) makes them particularly suitable for dynamic fault risk assessment and Remaining Useful Life (RUL) prediction in electric drive systems, consequently providing decision support for formulating optimized maintenance strategies [2].

1.4. ⽂献综述的⽬的、范围与结构
1.4. Purpose, Scope, and Structure of the Literature Review

本综述旨在系统性地梳理、 分析和评价当前利⽤贝叶斯⽹络及其相关理论进⾏新能源汽⻋电⼒驱
This review aims to systematically organize, analyze, and evaluate current research on applying Bayesian Networks and related theories to power drive systems in new energy vehicles,

动系统动态故障⻛险预测与维护决策的研究现状。具体范围将聚焦于贝叶斯⽹络及其扩展模型
Research status of dynamic fault risk prediction and maintenance decision-making for drive systems. The specific scope will focus on Bayesian networks and their extended models

（如动态贝叶斯⽹络DBN、 贝叶斯神经⽹络BNN） 在电⼒驱动系统（包括电机、控制器、功率电 ⼦器件等关键部件） 的故障预测、 RUL估计以及维护策略优化⽅⾯的应⽤ 。本综述的撰写背景 ， 部分参考了《机械⼯程前沿课程》 的报告要求 ，⼒求达到学术性的深度和⼴度 1。
(such as Dynamic Bayesian Networks DBN and Bayesian Neural Networks BNN) in applications for fault prediction, Remaining Useful Life (RUL) estimation, and maintenance strategy optimization in electric drive systems (including key components such as motors, controllers, and power electronic devices). The background for this review partially references the reporting requirements of the "Frontiers in Mechanical Engineering" course, striving to achieve both academic depth and breadth [1].

本⽂结构安排如下： 第⼆章将探讨新能源汽⻋电⼒驱动系统的主要故障机理以及传统可靠性分析 ⽅法的应⽤与局限； 第三章介绍贝叶斯⽹络的基本理论及其在可靠性⼯程中的应⽤基础； 第四章 重点综述基于贝叶斯⽹络的电⼒驱动系统动态故障⻛险预测⽅法； 第五章讨论如何利⽤贝叶斯⽹ 络进⾏电⼒驱动系统的维护决策优化； 第六章介绍贝叶斯⽹络相关的⾼级⽅法及其在电⼒驱动系 统可靠性分析中的应⽤前景； 第七章总结当前研究⾯临的挑战并展望未来的研究⽅向； 最后为结 论部分。
The structure of this paper is arranged as follows: Chapter 2 will explore the main fault mechanisms of new energy vehicle electric drive systems and the applications and limitations of traditional reliability analysis methods; Chapter 3 introduces the fundamental theory of Bayesian networks and their application basis in reliability engineering; Chapter 4 comprehensively reviews Bayesian network-based methods for dynamic fault risk prediction in electric drive systems; Chapter 5 discusses how to utilize Bayesian networks for maintenance decision optimization in electric drive systems; Chapter 6 introduces advanced methods related to Bayesian networks and their application prospects in reliability analysis of electric drive systems; Chapter 7 summarizes current research challenges and prospects for future research directions; finally, the conclusion section.

2. 新能源汽车电⼒驱动系统故障机理与传统可靠性分析⽅法
2. Fault Mechanisms of New Energy Vehicle Electric Drive Systems and Traditional Reliability Analysis Methods

深⼊理解新能源汽⻋电⼒驱动系统的故障机理是进⾏有效⻛险预测和维护的基础。 同时， 回顾传统可靠性分析⽅法及其局限性 ，有助于阐明引⼊贝叶斯⽹络等先进技术的必要性。
A thorough understanding of the failure mechanisms in new energy vehicle electric drive systems forms the foundation for effective risk prediction and maintenance. Simultaneously, reviewing traditional reliability analysis methods and their limitations helps clarify the necessity of introducing advanced technologies such as Bayesian networks.

2.1. 主要部件及其典型故障模式
2.1. Key Components and Their Typical Failure Modes

新能源汽⻋电⼒驱动系统由多个精密部件构成 ，每个部件都有其特定的故障模式和主导失 效载荷。
The electric drive system of new energy vehicles consists of multiple precision components, each with its specific failure modes and dominant failure loads.

● 2.1.1. 电机 (Motor)
● 2.1.1. Electric Motor

驱动电机是电⼒驱动系统的核⼼动⼒输出单元 ，其常见故障模式包括：
The drive motor is the core power output unit of the electric drive system, and its common failure modes include:

○ 定⼦故障：绕组短路（ 匝间、相间、对地短路） 、绝缘⽼化或击穿是定⼦最常见 的故障形式 ，通常由过热、过电压、机械应⼒或制造缺陷引起 1。
○ Stator faults: Winding short circuits (inter-turn, inter-phase, or ground faults), insulation aging, or breakdown are the most common stator failure forms, typically caused by overheating, overvoltage, mechanical stress, or manufacturing defects [1].

○ 转⼦故障：对于异步电机 ，转⼦导条断裂或端环开焊是主要问题；对于永磁同步 电机 (PMSM)，永磁体⾼温退磁、磁钢松动或断裂、转⼦偏⼼等较为常见 1。
○ Rotor faults: For induction motors, broken rotor bars or cracked end rings are the main issues; for permanent magnet synchronous motors (PMSM), high-temperature demagnetization of permanent magnets, loose or fractured magnets, and rotor eccentricity are more prevalent [1].

○ 轴承故障：轴承的磨损、疲劳、点蚀、保持架损坏甚⾄烧损是电机系统的频发故 障 ，主要由润滑不良、过载、安装不当、污染或⻓期运⾏在⾼温环境下导致 1。
○ Bearing faults: Wear, fatigue, pitting, cage damage, or even burning of bearings are frequent motor system failures, primarily caused by poor lubrication, overload, improper installation, contamination, or prolonged operation in high-temperature environments [1].

电机的主要失效主导载荷包括变化的转矩、宽范围的转速以及显著的电磁和热负 载 1。
The main failure-inducing loads of electric motors include variable torque, wide-ranging rotational speeds, and significant electromagnetic and thermal loads 1.

● 2.1.2. 控制器 (Controller)
● 2.1.2. Controller

电机控制器是电⼒驱动系统的“⼤脑” ，负责精确控制电机的运⾏ ，其故障直接影响⻋ 辆性能和安全。
The motor controller serves as the "brain" of the electric drive system, responsible for precise control of motor operation. Its failures directly impact vehicle performance and safety.

○ 功率模块（如IGBT）故障：IGBT作为关键的功率开关器件 ，其开路、短路、参 数漂移、封装破裂或烧毁等故障模式较为突出。这些故障通常由过电流、过电 压、过热（特别是频繁的热循环） 、静电、驱动信号异常或器件⽼化引起 1。
○ Power module (e.g., IGBT) failures: As critical power switching devices, IGBTs exhibit prominent failure modes such as open circuits, short circuits, parameter drift, package cracking, or burnout. These failures are typically caused by overcurrent, overvoltage, overheating (particularly frequent thermal cycling), electrostatic discharge, abnormal drive signals, or device aging 1.

○ 电容器故障：⺟线⽀撑电容和滤波电容的性能退化（容量下降、等效串联电阻 ESR增⼤） 、漏液、击穿或烧毁 ，会影响控制器的稳定性和寿命 1。
○ Capacitor failure: Performance degradation of bus support capacitors and filter capacitors (reduced capacitance, increased equivalent series resistance ESR), leakage, breakdown, or burnout can affect the stability and lifespan of the controller 1.

○ 控制逻辑/电路板故障：控制芯⽚ 、驱动芯⽚ 、采样电路等电⼦元器件的失效 ，以 及电路板的虚焊、 断路、腐蚀等问题 1。
○ Control logic/circuit board failure: Malfunctions of electronic components such as control chips, driver chips, and sampling circuits, as well as issues like cold solder joints, open circuits, and corrosion on circuit boards 1.

○ 传感器故障： 电流传感器、 电压传感器、温度传感器、霍尔位置传感器等的失效
○ Sensor failure: Malfunctions or signal abnormalities in current sensors, voltage sensors, temperature sensors, Hall position sensors, etc.

或信号异常 ，会导致控制策略紊乱 ，甚⾄系统停机 1。 控制器的主导失效载荷主 要为电流应⼒ 、 电压应⼒以及由此产⽣的热应⼒ 1。
can lead to control strategy disorders and even system shutdown 1. The dominant failure loads of the controller are primarily current stress, voltage stress, and the resulting thermal stress 1.

● 2.1.3. 传动机构 (Transmission components)
● 2.1.3. Transmission Components

虽然部分新能源汽⻋采⽤电机直驱 ，但许多⻋型仍包含减速器等传动部件。
Although some new energy vehicles employ direct motor drive, many models still incorporate transmission components such as reducers.

○ 齿轮故障：齿⾯疲劳磨损、点蚀、胶合、 断齿等是齿轮的主要失效形式 1。
○ Gear failures: Tooth surface fatigue wear, pitting, scuffing, and tooth breakage are the primary failure modes of gears [1].

○ 轴类零件故障：驱动轴、花键等在传递扭矩过程中可能发⽣疲劳断裂或磨损 1。 传动机构的主导失效载荷为扭矩和转速。
○ Shaft component failures: Drive shafts, splines, and similar components may experience fatigue fractures or wear during torque transmission [1]. The dominant failure-inducing loads for transmission mechanisms are torque and rotational speed.

● 2.1.4. 其他关键部件 (Other critical components)
● 2.1.4. Other Critical Components

○ ⾼压配电单元 (PDU)：包含熔断器、继电器、接触器、预充电阻和连接器等 ，这 些部件的失效（如触点熔焊、线圈烧毁、接触不良） 会影响⾼压电能的分配与安 全 1。
○ High-voltage power distribution unit (PDU): Contains fuses, relays, contactors, precharge resistors, and connectors. Failures of these components (such as contact welding, coil burnout, poor contact) can affect high-voltage power distribution and safety [1].

○ 连接器与线束：⾼压连接器的接触不良、松动、氧化 ，以及线束的绝缘破损、⽼ 化等 ，可能导致系统短路、 断路或性能下降。
○ Connectors and wiring harnesses: Poor contact, loosening, oxidation of high-voltage connectors, as well as insulation damage and aging of wiring harnesses may lead to system short circuits, open circuits, or performance degradation.

下表2.1总结了新能源汽⻋电⼒驱动系统关键部件、其主导失效载荷及常见故障模式。 表2.1: 新能源汽⻋电⼒驱动系统关键部件、主导载荷及常见故障模式
Table 2.1 summarizes the key components of new energy vehicle electric drive systems, their dominant failure loads, and common failure modes. Table 2.1: Key Components, Dominant Loads, and Common Failure Modes of New Energy Vehicle Electric Drive Systems

此表为理解电⼒驱动系统故障提供了基础 ，为后续讨论贝叶斯⽹络如何预测这些故障奠定 了基础。
This table provides a foundation for understanding electric drive system failures and lays the groundwork for subsequent discussion on how Bayesian networks can predict these faults.

2.2. 传统可靠性分析⽅法
2.2 Traditional Reliability Analysis Methods

在贝叶斯⽹络等先进⽅法得到⼴泛应⽤之前 ，传统的可靠性分析⽅法如故障模式与影响分 析 (FMEA)和故障树分析 (FTA) 已在⼯程领域扮演了重要角⾊。
Before advanced methods such as Bayesian networks gained widespread application, traditional reliability analysis approaches like Failure Mode and Effects Analysis (FMEA) and Fault Tree Analysis (FTA) had played significant roles in engineering fields.

● 2.2.1. 故障模式与影响分析 (FMEA)
● 2.2.1 Failure Mode and Effects Analysis (FMEA)

FMEA是⼀种⾃下⽽上、系统化的定性分析⽅法 ， 旨在识别产品或过程中潜在的故障 模式 ，分析其可能的原因和造成的影响 ，并评估其风险程度 ，从⽽采取预防或纠正措 施 1。在电⼒驱动系统的设计和开发阶段 ，FMEA被⽤于识别关键部件的潜在故障模 式 ，例如电机轴承的磨损、IGBT的过热烧毁等 ，并分析这些故障对系统性能、安全 性和可靠性的影响 1。通过计算风险优先数 (RPN)，可以确定需要优先关注和改进的 薄弱环节。
FMEA is a bottom-up, systematic qualitative analysis method designed to identify potential failure modes in products or processes, analyze their possible causes and consequences, and assess their risk levels to implement preventive or corrective measures [1]. During the design and development phases of electric drive systems, FMEA is employed to identify potential failure modes of critical components, such as motor bearing wear and IGBT overheating burnout, while analyzing the impact of these failures on system performance, safety, and reliability [1]. By calculating the Risk Priority Number (RPN), priority areas requiring attention and improvement can be determined.

● 2.2.2. 故障树分析 (FTA)
● 2.2.2. Fault Tree Analysis (FTA)

FTA是⼀种⾃上⽽下的演绎式故障分析⽅法 ，它从系统不希望发⽣的顶事件（如“ 电 ⼒驱动系统失效”） 开始 ，逐级向下分析导致顶事件发⽣的直接原因事件 ，直到找到
FTA is a top-down deductive failure analysis method that begins with an undesired top event of the system (such as "failure of electric drive system") and progressively analyzes the direct causal events leading to the top event until identifying

基本事件（如元器件故障、⼈为失误等），并⽤逻辑⻔ （与⻔ 、或⻔等）表⽰这些事 件之间的逻辑关系 ，最终形成⼀个树状的逻辑图 1。FTA不仅可以进⾏定性分析 ，找 出导致系统失效的最⼩割集（即导致顶事件发⽣的最简单的基本事件组合），还可以 通过赋予基本事件失效率或失效概率 ，进⾏定量分析 ，计算顶事件发⽣的概率 1。
basic events (such as component failures, human errors, etc.). These events are connected using logic gates (AND gates, OR gates, etc.) to represent their logical relationships, ultimately forming a tree-like logic diagram [1]. FTA can not only perform qualitative analysis to identify minimal cut sets (i.e., the simplest combinations of basic events that cause the top event) but also conduct quantitative analysis by assigning failure rates or probabilities to basic events to calculate the probability of the top event occurring [1].

2.3. 传统⽅法的局限性与挑战
2.3. Limitations and Challenges of Traditional Methods

尽管FMEA和FTA等传统⽅法在可靠性⼯程中具有重要价值 ，但它们在应⽤于复杂动态系 统如新能源汽⻋电⼒驱动系统时 ，也暴露出⼀些固有的局限性：
Although traditional methods such as FMEA and FTA hold significant value in reliability engineering, they also reveal some inherent limitations when applied to complex dynamic systems like new energy vehicle electric drive systems:

● 静态性：FMEA和FTA本质上是静态分析⼯具 ，难以有效模拟系统随时间变化的动态 ⾏为、部件的性能退化过程以及时间依赖性故障 12。 它们通常假设部件的失效率为常
● Static Nature: FMEA and FTA are inherently static analysis tools that struggle to effectively simulate the dynamic behavior of systems over time, the performance degradation process of components, and time-dependent failures. They typically assume constant failure rates for components.

数 ，这与电⼒驱动系统在实际运⾏中承受动态载荷、经历磨损⽼化过程的现实不符 1
This does not align with the reality that electric drive systems experience dynamic loads and undergo wear-and-tear aging processes during actual operation [1]

。

● 不确定性处理能⼒有限：传统⽅法在处理不确定信息 ，如数据不完整、专家知识模糊 等⽅⾯能⼒有限 1。例如 ，获取精确的部件失效率数据对于新技术和新部件⽽⾔往往 ⾮常困难 1。
● Limited capability in handling uncertainty: Traditional methods have restricted capacity when dealing with uncertain information, such as incomplete data or ambiguous expert knowledge. For instance, obtaining precise component failure rate data is often extremely challenging for new technologies and components.

● 模型复杂性：对于包含⼤量部件和复杂相互作⽤的系统 ，构建的故障树可能变得异常 庞⼤和复杂 ，难以分析和维护 13。
● Model Complexity: For systems containing numerous components and complex interactions, the constructed fault trees may become exceptionally large and intricate, making analysis and maintenance challenging [13].

● 数据依赖性：FTA的定量分析⾼度依赖于基本事件的精确失效率数据 ，⽽这些数据对 于新兴的电⼒驱动系统部件可能并不充分或难以获得 1。
● Data Dependency: The quantitative analysis of FTA highly depends on precise failure rate data of basic events, which may be insufficient or difficult to obtain for emerging electric drive system components [1].

● 多态⾏为建模困难：传统⽅法通常将部件状态简化为“正常”和“失效”两种 ，难以描述 部件从正常到完全失效之间的多种中间退化状态或性能⽔平 1。
● Difficulty in Modeling Multi-State Behavior: Traditional methods typically simplify component states into just "normal" and "failed," making it difficult to describe various intermediate degradation states or performance levels between normal operation and complete failure [1].

● 缺乏学习能⼒： FMEA和FTA模型⼀旦建⽴ ，通常是固定的 ，缺乏从新的运⾏数据中 学习和更新模型参数的能⼒。
● Lack of Learning Capability: Once established, FMEA and FTA models are typically fixed and lack the ability to learn from new operational data or update model parameters.

这些局限性在新能源汽⻋电⼒驱动系统这类新兴且快速发展的技术领域尤为突出。 电⼒驱 动系统的故障机理不仅复杂 ，⽽且深受动态运⾏⼯况（如驾驶员⾏为、充电模式）和环境 因素的影响 1。这些因素难以在静态的FMEA/FTA模型中得到充分体现 ，从⽽催⽣了对能 够处理动态性、不确定性并能从数据中学习的先进可靠性分析⽅法的需求。
These limitations are particularly prominent in emerging and rapidly developing technological fields such as new energy vehicle electric drive systems. The failure mechanisms of electric drive systems are not only complex but also significantly influenced by dynamic operating conditions (e.g., driver behavior, charging modes) and environmental factors. These factors are difficult to fully capture in static FMEA/FTA models, thereby creating a demand for advanced reliability analysis methods capable of handling dynamics, uncertainty, and learning from data.

3. 贝叶斯⽹络理论及其在可靠性⼯程中的应⽤基础
3. Bayesian Network Theory and Its Foundational Applications in Reliability Engineering

⾯对传统可靠性分析⽅法的局限性 ，贝叶斯⽹络 (BN)提供了⼀种更为灵活和强⼤的框架 , ⽤于处理复杂系统中的不确定性和依赖关系。
Faced with the limitations of traditional reliability analysis methods, Bayesian Networks (BN) provide a more flexible and powerful framework for handling uncertainty and dependencies in complex systems.

3.1. 贝叶斯⽹络基本原理
3.1. Fundamental Principles of Bayesian Networks

● 3.1.1. 图结构与参数学习
● 3.1.1. Graph Structure and Parameter Learning

贝叶斯⽹络是⼀种有向⽆环图 (Directed Acyclic Graph, DAG)，其中节点代表系统中 的随机变量（如部件状态、传感器读数、故障模式等），有向边则表⽰变量之间的直 接因果关系或条件依赖关系 1。BN的结构可以通过领域专家的知识构建 ，也可以从历
Bayesian networks are a type of directed acyclic graph (DAG) where nodes represent random variables in a system (such as component states, sensor readings, failure modes, etc.), and directed edges indicate direct causal relationships or conditional dependencies between variables. The structure of a BN can be constructed based on domain expert knowledge or learned from historical...

史数据中学习得到 ，或者采⽤两者的混合⽅法 1。BN的参数 ，即条件概率表
can be learned from historical data, or obtained using a hybrid approach of both methods 1. The parameters of BN, namely the conditional probability tables

(Conditional Probability Tables, CPTs)，量化了⼦节点状态对其⽗节点状态组合的依 赖程度。CPTs的确定同样可以基于统计数据（如部件的失效率数据） 或专家经验的 量化 1。
(Conditional Probability Tables, CPTs), which quantify the degree of dependence of child node states on combinations of parent node states. The determination of CPTs can also be based on statistical data (such as component failure rate data) or the quantification of expert experience [1].

● 3.1.2. 概率推理机制
● 3.1.2. Probabilistic Reasoning Mechanism

贝叶斯⽹络的核⼼推理机制基于贝叶斯定理 ，该定理描述了在获得新的证据（观测数 据）后如何更新对某个假设（如某个部件发⽣故障） 的置信度 1。BN⽀持多种类型的 概率推理：
The core reasoning mechanism of Bayesian networks is based on Bayes' theorem, which describes how to update the confidence in a hypothesis (such as a component failure) after obtaining new evidence (observed data). BNs support multiple types of probabilistic reasoning:

○ 诊断推理 (Diagnostic Reasoning)：从观察到的系统现象（ “果”）推断其可能 的原因（ “ 因”），例如 ，根据电机异常振动和过热现象 ，推断轴承故障的概率。
○ Diagnostic Reasoning: Inferring potential causes ("effects") from observed system phenomena ("causes"), for example, determining the probability of bearing failure based on abnormal motor vibration and overheating symptoms.

○ 预测推理 (Predictive Reasoning)：从已知的原因（ “ 因”）推断其可能导致的后 果（ “果”），例如 ，在已知IGBT模块温度持续偏⾼的情况下 ，预测其未来发⽣故 障的概率。
○ Predictive Reasoning: Predicting potential consequences ("effects") from known causes ("causes"), for example, estimating the probability of future failure when the IGBT module temperature remains consistently high.

○ 因果间推理 (Intercausal Reasoning)：分析多个原因对同⼀结果的综合影响。
○ Intercausal Reasoning: Analyzing the combined influence of multiple causes on the same outcome.

推理算法包括精确推理（如变量消除法 ，1 中提及了其针对硬证据的优化）和近似 推理（如⻢尔可夫链蒙特卡洛 MCMC） 1。
Inference algorithms include exact inference (such as variable elimination, with optimizations for hard evidence mentioned in [1]) and approximate inference (e.g., Markov Chain Monte Carlo - MCMC) [1].

3.2. 贝叶斯⽹络在可靠性建模中的优势
3.2. Advantages of Bayesian Networks in Reliability Modeling

与传统⽅法相⽐ ，贝叶斯⽹络在电⼒驱动系统等复杂系统的可靠性建模中展现出多⽅⾯优 势：
Compared with traditional methods, Bayesian networks demonstrate multifaceted advantages in reliability modeling for complex systems such as electric drive systems:

● 强⼤的依赖关系与因果关系建模能⼒： BN能够清晰地表⽰部件间、故障模式间以及 故障与征兆间的复杂依赖和因果链条 1。
● Powerful dependency and causality modeling capability: BN can clearly represent complex dependencies and causal chains between components, failure modes, and between failures and symptoms 1.

● 多源信息融合：BN框架允许⾃然地融合不同类型和来源的信息 ，包括定性的专家经 验（如通过模糊概率设定先验）和定量的历史数据（ ⽤于学习CPTs）1。
● Multi-source information fusion: The BN framework naturally allows for the integration of different types and sources of information, including qualitative expert knowledge (e.g., through fuzzy probability setting of priors) and quantitative historical data (used for learning CPTs) 1.

● 不确定性的概率化处理：BN以概率的⽅式显式处理各种不确定性 ，如数据的不完备 性、测量噪声、模型参数的不确定性等 1。
● Probabilistic handling of uncertainty: BN explicitly processes various uncertainties in a probabilistic manner, such as incomplete data, measurement noise, and uncertainty in model parameters 1.

● 灵活的双向推理能⼒： ⽀持从故障到原因的诊断和从原因到故障的预测 ，这对于故障 排查和风险预警都⾄关重要 1。
● Flexible bidirectional reasoning capability: Supports both diagnosis from failure to cause and prediction from cause to failure, which is crucial for fault troubleshooting and risk warning 1.

● 模型可解释性：BN的图形化结构直观易懂 ，有助于领域专家理解模型的逻辑和推理 过程 ，增强了模型的可信度和可接受性 5。
● Model Interpretability: The graphical structure of BN is intuitive and easy to understand, helping domain experts comprehend the model's logic and reasoning process, thereby enhancing the model's credibility and acceptability.

● 模型更新与学习能⼒： BN模型可以随着新数据的获得⽽动态更新其参数甚⾄结构，
● Model Updating and Learning Capability: The BN model can dynamically update its parameters and even its structure as new data is acquired,

使其能够适应系统状态的变化和知识的积累。这种“活性模型” 的特性 ，与新能源汽⻋ 技术和故障认知不断发展的现状⾼度契合 ，是其相较于静态FMEA/FTA⽂档的显著进 步。
enabling it to adapt to changes in system states and the accumulation of knowledge. This characteristic of a "living model" aligns well with the continuous development of new energy vehicle technology and fault cognition, representing a significant advancement over static FMEA/FTA documents.

3.3. 构建贝叶斯⽹络模型
3.3. Constructing Bayesian Network Models

构建⼀个有效的贝叶斯⽹络模型通常涉及结构构建和参数学习两个主要步骤。
Constructing an effective Bayesian network model typically involves two main steps: structure construction and parameter learning.

● 3.3.1. 结构构建：专家知识与数据驱动
● 3.3.1. Structure Construction: Expert Knowledge and Data-Driven Approaches

BN的拓扑结构定义了变量间的条件独⽴关系。结构构建的⽅法主要有：
The topological structure of a BN defines the conditional independence relationships between variables. The primary methods for structure construction include:

○ 专家驱动：依赖领域专家的经验和知识来确定相关的变量节点以及它们之间的因 果连接。这种⽅法在缺乏充⾜历史数据 ，尤其是在分析新技术或罕⻅故障模式时 ⾮常有效 1。
○ Expert-driven approach: Relies on domain experts' experience and knowledge to determine relevant variable nodes and their causal connections. This method proves particularly effective when sufficient historical data is lacking, especially when analyzing new technologies or rare failure modes 1.

○ 数据驱动：利⽤机器学习算法（如基于评分的⽅法、基于约束的⽅法） 从⼤量的 观测数据中⾃动学习⽹络结构。
○ Data-driven: Utilizes machine learning algorithms (such as score-based methods, constraint-based methods) to automatically learn network structures from large amounts of observational data.

○ 混合⽅法：结合专家知识设定初始结构或约束条件 ，再利⽤数据进⾏结构的优化 和调整 ，是⽬前较为推崇的⽅式。 对于新能源汽⻋电⼒驱动系统 ，其部件和故障 机理的复杂性决定了BN结构构建往往需要专家知识的深度参与 ，尤其是在系统开 发的早期阶段或针对新型部件时 ，数据的稀缺性使得纯数据驱动⽅法难以奏效。
○ Hybrid methods: Combines expert knowledge to establish initial structures or constraints, then uses data for structural optimization and adjustment—currently the more advocated approach. For new energy vehicle power drive systems, the complexity of their components and failure mechanisms dictates that BN structure construction often requires deep expert involvement, particularly during early development stages or for novel components, where data scarcity renders purely data-driven methods ineffective.

随着运⾏数据的积累 ，数据驱动的⽅法可以⽤于验证和优化专家构建的初始结
As operational data accumulates, data-driven methods can be employed to validate and optimize the initial structures built by experts.

构。
structure.

● 3.3.2. 参数学习：数据与先验知识融合
● 3.3.2. Parameter Learning: Integration of Data and Prior Knowledge

参数学习的⽬标是量化BN结构中定义的依赖关系 ，即确定CPTs中的概率值。
The objective of parameter learning is to quantify the dependency relationships defined in the BN structure, i.e., to determine the probability values in the CPTs.

○ 基于数据估计： 当有充⾜的历史故障数据或实验数据时 ，可以使⽤统计⽅法（如 最⼤似然估计、贝叶斯估计）来估计CPT参数。
○ Data-driven estimation: When sufficient historical failure data or experimental data is available, statistical methods (such as maximum likelihood estimation or Bayesian estimation) can be used to estimate CPT parameters.

○ 专家知识融⼊：在数据不⾜的情况下 ，特别是对于发⽣概率较低的故障事件或新 研发的系统部件 ，可以邀请领域专家根据其经验给出先验概率分布或CPT的初始 估计。这些专家意⻅可以通过直接赋值、概率elicitation技术或模糊集理论等⽅ 式融⼊模型 1。
○ Incorporation of expert knowledge: In cases of insufficient data, particularly for low-probability failure events or newly developed system components, domain experts can be invited to provide prior probability distributions or initial estimates of CPTs based on their experience. These expert opinions can be integrated into the model through direct assignment, probability elicitation techniques, or fuzzy set theory, among other methods.

3.4. 多态系统与不确定性处理
3.4. Multi-State Systems and Uncertainty Handling

贝叶斯⽹络在处理部件多态⾏为和数据不确定性⽅⾯具有天然优势。
Bayesian networks possess inherent advantages in handling component multi-state behaviors and data uncertainties.

● 3.4.1. 离散与连续变量处理
● 3.4.1. Discrete and Continuous Variable Processing

电⼒驱动系统中的部件状态往往不仅仅是“正常”或“失效”⼆元状态 ，⽽是可能经历多 个退化阶段 ，如“正常”、“轻微磨损”、“ 中度磨损”、“严重磨损”、“完全失效”等。
Components in electric drive systems often exhibit more than just binary states of "normal" or "failed," but may undergo multiple degradation stages such as "normal," "slight wear," "moderate wear," "severe wear," and "complete failure."

BN可以⽅便地通过定义离散的多状态节点来描述这种多态⾏为 1。对于连续型变量 （如温度、振动幅值），可以通过离散化技术将其转换为BN能够处理的离散状态， 或者采⽤连续型贝叶斯⽹络（如⾼斯贝叶斯⽹络）。
BN can conveniently describe such multi-state behaviors by defining discrete multi-state nodes 1. For continuous variables (such as temperature, vibration amplitude), they can be converted into discrete states that BN can handle through discretization techniques, or continuous Bayesian networks (such as Gaussian Bayesian networks) can be employed.

● 3.4.2. 模糊贝叶斯⽹络 (Fuzzy Bayesian Networks)
● 3.4.2. Fuzzy Bayesian Networks

为了更好地处理专家知识中存在的模糊性和不精确性 ，可以将模糊集理论与贝叶斯⽹ 络相结合 ，形成模糊贝叶斯⽹络。在这种⽹络中 ，节点的先验概率或CPT中的概率值 可以⽤模糊数（如三⻆模糊数） 或语⾔变量（如“⾼ ”、“ 中”、“低”）来表⽰ ，并通过 模糊推理和解模糊化技术进⾏概率计算 1。例如 ，1详细介绍了如何通过专家打分法 获取节点先验概率的模糊表⽰ ，并使⽤三⻆模糊数进⾏量化。这种⽅法增强了BN处 理主观不确定信息的能⼒。
To better handle the fuzziness and imprecision in expert knowledge, fuzzy set theory can be combined with Bayesian networks to form fuzzy Bayesian networks. In such networks, the prior probabilities of nodes or the probability values in CPTs can be represented using fuzzy numbers (such as triangular fuzzy numbers) or linguistic variables (such as "high," "medium," "low"), and probability calculations are performed through fuzzy reasoning and defuzzification techniques 1. For example, 1 details how to obtain fuzzy representations of node prior probabilities through expert scoring methods and uses triangular fuzzy numbers for quantification. This approach enhances BN's ability to handle subjective uncertain information.

下表3.1对⽐了传统可靠性分析⽅法（ FMEA/FTA） 与贝叶斯⽹络在电⼒驱动系统分析中的 关键特性。
Table 3.1 compares the key characteristics of traditional reliability analysis methods (FMEA/FTA) and Bayesian networks in the analysis of electric drive systems.

表3.1: 传统可靠性⽅法 (FMEA/FTA) 与贝叶斯⽹络在电⼒驱动系统分析中的⽐较
Table 3.1: Comparison between Traditional Reliability Methods (FMEA/FTA) and Bayesian Networks in Electric Drive System Analysis

此表清晰地展⽰了贝叶斯⽹络在处理新能源汽⻋电⼒驱动系统这类复杂动态系统时 ，相对 于传统⽅法的优势 ，从⽽确⽴了其作为本综述核⼼技术的理由。
This table clearly demonstrates the advantages of Bayesian networks over traditional methods when handling complex dynamic systems like new energy vehicle power drive systems, thereby establishing its rationale as the core technology of this review.

4. 基于贝叶斯⽹络的电⼒驱动系统动态故障风险预测
4. Dynamic Fault Risk Prediction for Power Drive Systems Based on Bayesian Networks

利⽤贝叶斯⽹络 ，特别是其动态扩展形式——动态贝叶斯⽹络 (DBN) ，可以有效地对新能 源汽⻋电⼒驱动系统的故障风险进⾏动态预测。这依赖于对系统运⾏数据的实时分析和对 部件健康状态演变过程的建模。
Using Bayesian networks, particularly their dynamic extension—Dynamic Bayesian Networks (DBN), enables effective dynamic prediction of fault risks in new energy vehicle power drive systems. This relies on real-time analysis of system operation data and modeling of component health state evolution processes.

4.1. 动态贝叶斯⽹络 (DBN) 及其在故障预测中的应⽤
4.1 Dynamic Bayesian Networks (DBN) and Their Applications in Fault Prediction

● 4.1.1. DBN模型结构与时间序列数据处理
● 4.1.1 DBN Model Structure and Time Series Data Processing

动态贝叶斯⽹络 (DBN)是贝叶斯⽹络在时间维度上的扩展 ，专⻔⽤于对随时间演变 的随机过程进⾏建模和推理 6。DBN通常由⼀个表⽰初始状态的先验⽹络 (B0)和⼀ 个表⽰状态转移的模板⽹络 (B→) 组成。模板⽹络描述了相邻时间⽚ （time slices）
Dynamic Bayesian Networks (DBN) are temporal extensions of Bayesian networks, specifically designed for modeling and reasoning about stochastic processes that evolve over time [6]. A DBN typically consists of a prior network (B0) representing the initial state and a template network (B→) representing state transitions. The template network describes dependencies between variables across adjacent time slices,

之间变量的依赖关系 ，从⽽能够捕捉系统状态随时间变化的动态特性。在电⼒驱动系 统的故障预测中 ，DBN可以将来⾃⻋辆传感器的时间序列数据（如电流、 电压、温
thereby capturing the dynamic characteristics of system state changes over time. In fault prediction for electric drive systems, DBNs can process time-series data from vehicle sensors (such as current, voltage, temperature)

度、振动信号等）作为输⼊ ，通过推理预测未来某个时间点部件发⽣故障的概率或进 ⼊某种退化状态的概率。
(input parameters such as temperature, vibration signals, etc.) as inputs to infer and predict the probability of component failure or the likelihood of entering a certain degradation state at a future time point.

● 4.1.2. 基于运⾏数据的特征提取与选择
● 4.1.2. Feature Extraction and Selection Based on Operational Data

原始的传感器数据往往包含⼤量冗余信息和噪声 ，直接将其作为DBN的输⼊不仅会增 加模型的复杂性 ，还可能降低预测的准确性。 因此 ，从运⾏数据中提取与故障相关的 有效特征是构建⾼性能DBN预测模型的关键步骤 1。1详细介绍了⼀个基于300名⽤⼾ ⼀年运⾏数据构建电⼒驱动系统加速可靠性测试循环的⽅法 ，其中就包括了提取运⾏ ⽚段、构建与主要失效载荷相关的特征参数 ，并利⽤主成分分析 (PCA)等⽅法进⾏ 特征降维和⼯况聚类识别。这些预处理技术对于提升后续DBN模型的预测性能⾄关重 要。 常⽤的特征提取⽅法包括时域统计特征（均值、⽅差、峰值、峭度等） 、频域特 征（频谱分析、⼩波变换等） 以及⼀些针对特定故障模式的专⻔特征 14。特征选择 旨在从提取的众多特征中挑选出对故障预测最敏感、最相关的特征⼦集 ，以简化模型 并避免过拟合。
Raw sensor data often contains substantial redundant information and noise. Directly using it as input for DBNs not only increases model complexity but may also reduce prediction accuracy. Therefore, extracting effective features related to faults from operational data is a crucial step in building high-performance DBN prediction models. Reference [1] details a method for constructing accelerated reliability test cycles for electric drive systems based on one year of operational data from 300 users, which includes extracting operational segments, building feature parameters related to primary failure loads, and employing techniques like Principal Component Analysis (PCA) for feature dimensionality reduction and operational condition clustering. These preprocessing techniques are essential for enhancing the predictive performance of subsequent DBN models. Common feature extraction methods include time-domain statistical features (mean, variance, peak value, kurtosis, etc.), frequency-domain features (spectral analysis, wavelet transform, etc.), and specialized features targeting specific fault modes [14]. Feature selection aims to identify the most sensitive and relevant subset of features from the extracted pool to simplify the model and avoid overfitting.

4.2. 电⼒驱动系统关键部件的DBN故障预测模型案例
4.2. Case Study of DBN Fault Prediction Models for Key Components in Electric Drive Systems

⽂献中已有不少研究将DBN应⽤于电⼒驱动系统关键部件的故障预测 ，取得了积极的成 果。
Numerous studies in the literature have applied DBNs to fault prediction of critical components in electric drive systems, achieving positive results.

● 4.2.1. 电机故障预测
● 4.2.1. Motor Fault Prediction

针对驱动电机 ，DBN已被⽤于预测定⼦绕组绝缘⽼化、轴承磨损退化等故障。例如， 通过监测电机的电流信号、振动信号和温度数据 ，构建DBN模型来预测轴承剩余使⽤ 寿命或定⼦绝缘状态的演变 5。模型中的节点可以表⽰轴承的磨损程度、绝缘电阻值 等健康指标 ，以及相关的运⾏参数。
For drive motors, DBNs have been used to predict faults such as stator winding insulation aging and bearing wear degradation. For example, by monitoring the motor's current signals, vibration signals, and temperature data, DBN models are constructed to predict bearing remaining useful life or the evolution of stator insulation state 5. Nodes in the model can represent health indicators such as bearing wear degree and insulation resistance values, as well as related operating parameters.

● 4.2.2. 控制器（含功率模块IGBT） 故障预测
● 4.2.2. Controller (Including Power Module IGBT) Fault Prediction

电机控制器，特别是其核⼼功率模块IGBT，是电⼒驱动系统中的易损部件。DBN被 ⼴泛应⽤于IGBT的健康状态监测和故障预测。通过分析IGBT的结温、开关损耗、导 通压降等参数的时间序列数据 ，可以构建DBN模型来预测其⽼化程度、开路或短路故 障的发⽣概率 9。考虑到IGBT的失效往往与热循环密切相关 ，DBN模型需要能够捕捉 这种累积损伤效应。
The motor controller, particularly its core power module IGBT, is a vulnerable component in electric drive systems. DBN is widely applied for health monitoring and fault prediction of IGBTs. By analyzing time-series data of IGBT parameters such as junction temperature, switching losses, and conduction voltage drop, a DBN model can be constructed to predict its aging degree and the probability of open-circuit or short-circuit faults. Considering that IGBT failures are often closely related to thermal cycling, the DBN model needs to capture this cumulative damage effect.

● 4.2.3. 轴承与齿轮等机械部件故障预测
● 4.2.3. Fault Prediction for Mechanical Components such as Bearings and Gears

对于电⼒驱动系统中的机械部件 ，如轴承和齿轮 ，其磨损和疲劳是主要的失效模式。 1中提到 ，通过分析⽤⼾运⾏数据 ，识别典型⼯况 ，并结合部件的损伤累积模型（如 Miner-Palmgren理论），可以评估这些部件在不同⼯况下的损伤程度。这些损伤评 估结果可以作为DBN的输⼊ ，⽤于预测部件的RUL或失效风险。例如 ，DBN可以建模 轴承振动信号特征随时间的变化 ，以预测剥落故障的发⽣。
For mechanical components in electric drive systems, such as bearings and gears, wear and fatigue are the primary failure modes. As mentioned in [1], by analyzing operational data to identify typical working conditions and combining them with component damage accumulation models (such as Miner-Palmgren theory), the damage degree of these components under different conditions can be assessed. These damage assessment results can serve as inputs for DBN to predict the RUL or failure risk of the components. For example, DBN can model the temporal changes in bearing vibration signal characteristics to predict the occurrence of spalling faults.

4.3. 故障诊断与根源分析
4.3. Fault Diagnosis and Root Cause Analysis

除了故障预测 ，贝叶斯⽹络（包括DBN） 的强⼤诊断能⼒也是其在电⼒驱动系统可靠性
In addition to fault prediction, the powerful diagnostic capabilities of Bayesian networks (including DBNs) also contribute to their application in power drive system reliability

分析中的重要应⽤ 。当系统出现异常或故障征兆时 ，可以利⽤BN/DBN的反向推理功能 ， 结合观测到的证据（如传感器报警、性能参数异常），推断导致这些现象的最可能的故障 源或根本原因 1。例如 ，若电机控制器输出电流异常 ，BN可以帮助判断是IGBT模块故
Important applications in analysis. When a system exhibits anomalies or signs of failure, the backward reasoning capability of BN/DBN can be utilized, combined with observed evidence (such as sensor alarms or abnormal performance parameters), to infer the most likely source of failure or root cause. For example, if the motor controller's output current is abnormal, the BN can help determine whether the issue stems from an IGBT module failure.

障、 电流传感器故障还是控制逻辑错误的概率更⼤。 通过计算各潜在故障原因的后验概率 , 可以有效地缩⼩故障排查范围 ，提⾼维修效率。此外 ，BN还可以⽤于识别系统中的薄 弱环节 ，即那些对系统整体失效风险贡献最⼤的部件或故障模式 1。
whether it is more likely to be a power supply failure, current sensor fault, or control logic error. By calculating the posterior probabilities of potential failure causes, the troubleshooting scope can be effectively narrowed, thereby improving maintenance efficiency. Additionally, Bayesian Networks (BN) can be used to identify weak links in the system—those components or failure modes that contribute most significantly to the overall system failure risk [1].

4.4. 性能评估指标与模型验证
4.4. Performance Evaluation Metrics and Model Validation

对构建的BN/DBN故障预测模型的性能进⾏客观评估和有效验证是确保其在实际应⽤中可 靠性的必要环节。 常⽤的性能评估指标包括：
Objective evaluation and effective validation of the constructed BN/DBN fault prediction models are essential steps to ensure their reliability in practical applications. Commonly used performance evaluation metrics include:

● 分类/预测准确率 (Accuracy)：正确预测故障或正常状态的样本⽐例。
● Classification/Prediction Accuracy: The proportion of samples correctly predicted as faulty or normal states.

● 精确率 (Precision)：在所有被预测为故障的样本中 ，真正是故障的⽐例。
● Precision: The proportion of truly faulty samples among all samples predicted as faulty.

● 召回率 (Recall) / 灵敏度 (Sensitivity)：在所有实际发⽣故障的样本中 ，被成功预 测出来的⽐例。
● Recall/Sensitivity: The proportion of actual faulty samples that are successfully predicted.

● F1分数 (F1-Score)：精确率和召回率的调和平均值 ，综合评价模型的性能。
● F1-Score: The harmonic mean of precision and recall, providing a comprehensive evaluation of model performance.

● 受试者⼯作特征曲线 (ROC Curve) 与曲线下⾯积 (AUC)：评估模型在不同阈值下的 分类能⼒ ，AUC值越接近1，模型性能越好 10。
● Receiver Operating Characteristic Curve (ROC Curve) and Area Under the Curve (AUC): Assess the classification capability of a model at different thresholds. A model performs better when the AUC value is closer to 1.

● 均⽅根误差 (RMSE) / 平均绝对误差 (MAE)：⽤于评估RUL预测等回归任务的精度。
● Root Mean Square Error (RMSE) / Mean Absolute Error (MAE): Used to evaluate the accuracy of regression tasks such as RUL prediction.

模型验证通常采⽤的⽅法包括：
Commonly adopted model validation methods include:

● 交叉验证 (Cross-Validation)：将数据集划分为训练集和测试集 ，多次重复训练和 测试过程 ，以评估模型的稳定性和泛化能⼒ 22。
● Cross-Validation: The dataset is divided into training and test sets, with the training and testing process repeated multiple times to evaluate the model's stability and generalization capability.

● 独⽴数据集测试：在模型训练完成后 ，使⽤未参与训练的独⽴数据集进⾏测试 ，以检 验模型的实际应⽤效果。
● Independent Dataset Testing: After model training is completed, an independent dataset not involved in training is used for testing to examine the model's practical application performance.

● 与实际故障数据对⽐： 将模型的预测结果与⻋辆实际发⽣的故障记录进⾏对⽐ ，评估 模型的预测准确性和预警提前期。1 中通过台架试验验证了其构建的加速测试载荷谱 的有效性 ，类似地 ，BN/DBN模型的预测结果也需要通过实验或实⻋数据进⾏验证。
● Comparison with Actual Fault Data: The model's prediction results are compared with actual vehicle fault records to assess the prediction accuracy and early warning lead time. Reference [1] validated the effectiveness of its constructed accelerated test load spectrum through bench tests. Similarly, the prediction results of BN/DBN models also require verification through experiments or real vehicle data.

下表4.1概述了DBN在电⼒驱动系统关键部件故障预测中的⼀些典型应⽤ 。 表4.1: DBN在电⼒驱动系统部件故障预测中的应⽤概述
Table 4.1 below summarizes some typical applications of DBN in fault prediction for key components of electric drive systems. Table 4.1: Overview of DBN Applications in Fault Prediction for Electric Drive System Components

此表通过具体的部件、故障类型、输⼊数据、模型特点和预期性能 ，展⽰了DBN在电⼒
This table demonstrates the practical application potential of DBN in dynamic fault prediction for electric power

驱动系统动态故障预测中的实际应⽤潜⼒ 。值得注意的是 ，DBN模型的有效性在很⼤程
drive systems through specific components, fault types, input data, model characteristics, and expected performance. It is noteworthy that the effectiveness of DBN models largely depends

度上取决于从运⾏数据中提取的特征的质量和相关性。仅仅拥有⼤量数据是不够的 ，必须 通过复杂的信号处理和特征⼯程技术 ，才能构建出鲁棒的DBN预测模型。此外 ，新能源
on the quality and relevance of features extracted from operational data. Simply having large amounts of data is insufficient; robust DBN prediction models can only be constructed through sophisticated signal processing and feature engineering techniques. Furthermore, new energy

汽⻋电⼒驱动系统运⾏⼯况的⾮平稳性（ 由不同的驾驶风格、环境条件和部件⽼化引起） 对DBN模型提出了挑战 ，可能需要发展能够适应参数或结构随时间变化的⾃适应DBN模 型。
The non-stationary operating conditions of automotive electric drive systems (caused by varying driving styles, environmental conditions, and component aging) pose challenges to DBN models, potentially necessitating the development of adaptive DBN models capable of accommodating time-varying parameters or structures.

5. 基于贝叶斯⽹络的电⼒驱动系统维护决策优化
5. Maintenance Decision Optimization for Electric Drive Systems Based on Bayesian Networks

贝叶斯⽹络的预测能⼒ ，特别是其在剩余使⽤寿命 (RUL) 估计⽅⾯的应⽤ ，为优化新能 源汽⻋电⼒驱动系统的维护策略提供了坚实基础。通过从传统的基于固定周期的预防性维 护或故障后的响应式维护 ，转向基于状态的视情维护 (CBM)和主动的预测性维护 (PdM) , 可以显著提⾼系统的可⽤性、降低维护成本并增强运⾏安全性。
The predictive capability of Bayesian networks, particularly their application in Remaining Useful Life (RUL) estimation, provides a solid foundation for optimizing maintenance strategies of new energy vehicle electric drive systems. By transitioning from traditional fixed-interval preventive maintenance or reactive post-failure maintenance to condition-based maintenance (CBM) and proactive predictive maintenance (PdM), system availability can be significantly improved while reducing maintenance costs and enhancing operational safety.

5.1. 剩余使⽤寿命 (RUL) 预测
5.1. Remaining Useful Life (RUL) Prediction

RUL是指⼀个设备或部件从当前时刻到其功能失效或性能降低到预定阈值以下所剩余的预 期⼯作时间或循环次数 9。准确的RUL预测是实现PdM的核⼼。
RUL (Remaining Useful Life) refers to the expected remaining operational time or number of cycles before a device or component fails functionally or its performance degrades below a predetermined threshold 9. Accurate RUL prediction is the core of implementing PdM.

● 5.1.1. 基于BN/DBN的RUL预测⽅法
● 5.1.1. BN/DBN-based RUL Prediction Methods

动态贝叶斯⽹络 (DBN) ⾮常适合⽤于建模部件的性能退化过程并估计其RUL 9。通过 将部件的健康状态（如磨损量、裂纹⻓度、容量衰减等）表⽰为DBN中的隐变量 ，并 利⽤传感器监测数据（如振动、温度、 电流等）作为观测变量 ，DBN可以实时更新对 部件健康状态的估计 ，并预测其达到失效阈值的时间。⽂献中已有⼤量关于利⽤DBN 进⾏电池RUL预测的研究 14，其⽅法论同样可以应⽤于电⼒驱动系统中的其他关键
Dynamic Bayesian Networks (DBNs) are particularly suitable for modeling component degradation processes and estimating their RUL 9. By representing a component's health state (such as wear amount, crack length, capacity degradation, etc.) as hidden variables in a DBN and utilizing sensor monitoring data (such as vibration, temperature, current, etc.) as observed variables, DBNs can update the estimation of component health status in real-time and predict the time to reach failure thresholds. Numerous studies in the literature have explored DBN-based RUL prediction for batteries 14, and the same methodology can be applied to other critical components in electric drive systems,

部件 ，如IGBT模块 9、轴承和电机绕组等。RUL的计算通常需要预先定义⼀个或多个 失效阈值 ，例如 ， 电池容量衰减⾄初始容量的80% 1，或轴承振动幅值超过某个限
such as IGBT modules 9, bearings, and motor windings. RUL calculation typically requires predefining one or more failure thresholds, for example, when battery capacity degrades to 80% of its initial capacity 1, or when bearing vibration amplitude exceeds a certain limit.

值。
value.

● 5.1.2. 考虑不确定性的RUL估计
● 5.1.2. RUL Estimation Considering Uncertainty

贝叶斯⽹络的⼀个重要优势是其能够提供概率性的RUL预测结果 ，⽽不仅仅是⼀个点 估计值 14。这意味着BN可以输出RUL的概率分布（例如 ，均值和⽅差 ，或置信区
A key advantage of Bayesian networks is their ability to provide probabilistic RUL prediction results, rather than just a point estimate [14]. This means BN can output the probability distribution of RUL (e.g., mean and variance, or confidence intervals).

间），从⽽量化RUL预测的不确定性。这种不确定性信息对于维护决策⾄关重要 ，因 为它允许决策者根据风险偏好来权衡提前维护和延迟维护的利弊。
thereby quantifying the uncertainty in RUL prediction. This uncertainty information is crucial for maintenance decision-making, as it allows decision-makers to weigh the pros and cons of early versus delayed maintenance based on risk preferences.

5.2. 视情维护 (CBM) 与预测性维护 (PdM) 策略
5.2 Condition-Based Maintenance (CBM) and Predictive Maintenance (PdM) Strategies

● 5.2.1. 基于BN的维护时机决策
● 5.2.1 BN-Based Maintenance Timing Decision

BN/DBN的输出 ，如部件当前的故障概率、健康状态评估或RUL估计 ，可以直接⽤于
The outputs of BN/DBN, such as current component failure probability, health state assessment, or RUL estimation, can be directly used to

触发维护操作 2。例如 ，可以设定⼀个维护阈值： 当某个部件的预测RUL低于该阈值 , 或者其失效概率超过预设⽔平时 ，系统便⾃动⽣成维护请求。26和27讨论了如何 确定最优的维护阈值以及相关的决策过程。这种基于部件实际健康状况的维护⽅式， 避免了固定周期维护可能带来的过早维护（浪费资源） 或过晚维护（导致意外故
trigger maintenance operations. For instance, a maintenance threshold can be set: when the predicted RUL of a component falls below this threshold or its failure probability exceeds a preset level, the system automatically generates a maintenance request. References 26 and 27 discuss how to determine optimal maintenance thresholds and related decision-making processes. This health-condition-based maintenance approach avoids both premature maintenance (resource waste) and delayed maintenance (leading to unexpected failures) that may occur with fixed-period maintenance.

障）。
failure).

● 5.2.2. 维护成本效益分析
● 5.2.2. Maintenance Cost-Benefit Analysis

为了实现维护策略的整体优化 ，需要将维护决策与成本效益分析相结合。通过在BN 模型中集成成本参数（如预防性维护成本、故障修复成本、停机损失成本、备件成本 等），可以评估不同维护时机和维护措施下的预期总成本 28。决策的⽬标通常是最 ⼩化在整个系统⽣命周期内的总维护成本 ，或在给定的预算约束下最⼤化系统的可⽤ 性。BN的概率推理能⼒使其能够处理成本参数的不确定性 ，并进⾏基于风险的决
To achieve overall optimization of maintenance strategies, it is necessary to integrate maintenance decisions with cost-benefit analysis. By incorporating cost parameters (such as preventive maintenance costs, failure repair costs, downtime loss costs, and spare parts costs) into the BN model, the expected total costs under different maintenance timings and measures can be evaluated 28. The decision-making objective typically involves minimizing the total maintenance costs over the entire system lifecycle or maximizing system availability under given budget constraints. The probabilistic reasoning capability of BN enables it to handle uncertainties in cost parameters and facilitate risk-based decision-making.

策。
policy.

5.3. 维护策略优化案例
5.3. Maintenance Strategy Optimization Case Studies

尽管直接针对新能源汽⻋电⼒驱动系统的BN驱动的PdM完整案例在现有⽂献中尚不充分 , 但相关领域（如航空发动机、⼯业设备、 电池管理系统） 的研究已展⽰了其巨⼤潜⼒。 例如 ，2和3概述了利⽤BN进⾏预测性维护的框架。这些研究通常涉及以下步骤：
Although comprehensive case studies of BN-driven PdM specifically for new energy vehicle electric drive systems remain insufficient in existing literature, research in related fields (such as aircraft engines, industrial equipment, and battery management systems) has demonstrated its significant potential. For instance, [2] and [3] outline frameworks utilizing BNs for predictive maintenance. These studies typically involve the following steps:

1. 利⽤传感器数据和历史信息构建部件的BN/DBN退化模型。
1. Constructing BN/DBN degradation models for components using sensor data and historical information.

2. 实时监测部件状态 ，并利⽤BN/DBN更新健康评估和RUL预测。
2. Monitoring component status in real-time and updating health assessments and RUL predictions using BN/DBN.

3. 结合预设的维护阈值或成本模型 ，确定最优的维护时机和维护类型（如⼩修、⼤修或 更换）。
3. Determine the optimal maintenance timing and type (e.g., minor repair, major repair, or replacement) by combining preset maintenance thresholds or cost models.

4. 执⾏维护操作 ，并将维护结果反馈给BN/DBN模型 ，以持续改进其预测精度。
4. Perform maintenance operations and feed the results back to the BN/DBN model to continuously improve its prediction accuracy.

成功实施基于BN的PdM策略 ，关键在于准确定义失效阈值和构建合理的成本模型。BN提 供的概率性预测输出⾮常强⼤ ，但要将其转化为最优的维护决策 ，需要有清晰、可量化的 ⾏动标准。此外 ，新能源汽⻋电⼒驱动系统“最优”维护的动态性是⼀个重要挑战。最优决 策会随着⻋辆使⽤模式、部件健康状况的演变甚⾄外部因素（如备件可⽤性或任务关键
The successful implementation of BN-based PdM strategies hinges on accurately defining failure thresholds and constructing reasonable cost models. While the probabilistic predictive outputs provided by BN are highly powerful, transforming them into optimal maintenance decisions requires clear, quantifiable action criteria. Additionally, the dynamic nature of "optimal" maintenance for new energy vehicle power drive systems presents a significant challenge. Optimal decisions evolve with vehicle usage patterns, component health conditions, and even external factors such as spare parts availability or mission-critical requirements.

性） 的变化⽽变化。这表明需要⼀种能够由BN模型动态更新的⾃适应维护策略。
The variation changes with the (performance) changes. This indicates the need for an adaptive maintenance strategy that can be dynamically updated by the BN model.

下表5.1总结了基于BN/DBN的RUL估计和预测性维护在电⼒驱动系统相关部件中的⼀些⽅ 法。
Table 5.1 summarizes some methods for RUL estimation and predictive maintenance of power drive system components based on BN/DBN.

表5.1: 基于BN/DBN的电⼒驱动系统相关部件RUL估计与预测性维护⽅法
Table 5.1: BN/DBN-based RUL estimation and predictive maintenance methods for power drive system components

此表将BN的预测能⼒与具体的维护⾏动联系起来 ，展⽰了这些模型在提升电⼒驱动系统 维护效率和可靠性⽅⾯的实际价值。
This table links the predictive capabilities of BN with specific maintenance actions, demonstrating the practical value of these models in improving the maintenance efficiency and reliability of electric drive systems.

6. 贝叶斯⽹络相关⾼级⽅法及其应⽤展望
6. Advanced Bayesian Network Methods and Their Application Prospects

随着⼈⼯智能和机器学习技术的不断发展 ，基于⻉叶斯理论的⾼级⽅法 ，如⻉叶斯神经⽹ 络 (BNN)和物理信息神经⽹络 (PINN)，在解决复杂⼯程系统的可靠性问题⽅⾯展现出越 来越⼤的潜⼒ 。这些⽅法有望克服传统BN在某些⽅⾯的局限性 ，为新能源汽⻋电⼒驱动 系统的故障预测和维护决策带来新的突破。
With the continuous development of artificial intelligence and machine learning technologies, advanced Bayesian theory-based methods such as Bayesian Neural Networks (BNN) and Physics-Informed Neural Networks (PINN) are demonstrating increasing potential in addressing reliability issues of complex engineering systems. These methods are expected to overcome certain limitations of traditional Bayesian Networks (BN), bringing new breakthroughs in fault prediction and maintenance decision-making for new energy vehicle power drive systems.

6.1. 贝叶斯神经⽹络 (BNN) 在电⼒驱动系统可靠性中的应⽤
6.1. Application of Bayesian Neural Networks (BNN) in Power Drive System Reliability

● 6.1.1. BNN的原理与优势
● 6.1.1. Principles and Advantages of BNN

⻉叶斯神经⽹络 (BNN)是深度学习与⻉叶斯概率理论的结合体 1。与传统神经⽹络使
Bayesian Neural Networks (BNN) represent a fusion of deep learning and Bayesian probability theory [1]. Unlike conventional neural networks that...

⽤确定的权重和偏置不同 ，BNN将这些参数视为服从⼀定概率分布的随机变量。这使 得BNN不仅能够学习输⼊与输出之间的复杂⾮线性关系 ，还能对预测结果的不确定性 进⾏量化 4。BNN的主要优势包括：
Unlike deterministic weights and biases, BNNs treat these parameters as random variables following certain probability distributions. This enables BNNs not only to learn complex nonlinear relationships between inputs and outputs but also to quantify the uncertainty of predictions [4]. The main advantages of BNNs include:

○ 处理⼩样本数据：在数据量有限的情况下 ，BNN通过引⼊参数的先验分布 ，能够 做出⽐传统神经⽹络更稳健的推断 ，有效避免过拟合 1。
○ Handling small-sample data: With limited data, BNNs can make more robust inferences than traditional neural networks by introducing prior distributions of parameters, effectively avoiding overfitting [1].

○ 量化预测不确定性：BNN的输出是⼀个概率分布 ，⽽不仅仅是⼀个点估计 ，这对 于风险评估和决策⾄关重要。
○ Quantifying prediction uncertainty: The output of a BNN is a probability distribution rather than just a point estimate, which is crucial for risk assessment and decision-making.

○ 防⽌过拟合：⻉叶斯⽅法固有的正则化效应有助于提⾼模型的泛化能⼒ 。 BNN的 训练通常采⽤变分推断 (Variational Inference) 或⻢尔可夫链蒙特卡洛 (MCMC) 等⽅法来近似参数的后验分布 1。
○ Preventing overfitting: The inherent regularization effect of Bayesian methods helps improve model generalization. BNN training typically employs methods such as Variational Inference or Markov Chain Monte Carlo (MCMC) to approximate the posterior distribution of parameters [1].

● 6.1.2. BNN在故障预测与RUL估计中的研究进展
● 6.1.2. Research Progress of BNN in Fault Prediction and RUL Estimation

BNN已开始应⽤于各种⼯程系统的结构可靠性分析和健康预测（prognostics）领域 1。例如 ，有研究利⽤BNN对航空发动机的RUL进⾏预测 ，其⽅法论对于电⼒驱动系 统中的关键部件（如电机、IGBT模块） 的RUL估计具有借鉴意义 32。1中提出的基于 ⾃适应BNN的结构可靠性分析⽅法 ，能够处理单失效和多失效模式问题 ，通过主动学 习策略选择信息量最⼤的样本点来更新模型 ，从⽽在保证精度的同时提⾼计算效率 1 。这种⾃适应学习的能⼒对于处理电⼒驱动系统运⾏数据的不确定性和动态性⾮常有 价值。
BNNs have begun to be applied in structural reliability analysis and health prognostics for various engineering systems 1. For instance, studies have utilized BNNs to predict the RUL of aircraft engines, and the methodology holds reference value for RUL estimation of critical components (such as motors and IGBT modules) in electric drive systems 32. The adaptive BNN-based structural reliability analysis method proposed in 1 can address both single and multiple failure mode problems. By employing an active learning strategy to select the most informative sample points for model updating, it improves computational efficiency while maintaining accuracy 1. This adaptive learning capability is particularly valuable for handling the uncertainty and dynamics in operational data of electric drive systems.

6.2. 物理信息神经⽹络 (PINN) 与贝叶斯⽅法的潜在结合
6.2. Potential Integration of Physics-Informed Neural Networks (PINN) with Bayesian Methods

● PINN基础：物理信息神经⽹络 (PINN)是⼀种新兴的科学机器学习⽅法 ，它通过将控 制系统⾏为的物理定律（通常表⽰为偏微分⽅程PDEs或常微分⽅程ODEs） 直接嵌⼊ 到神经⽹络的损失函数中进⾏训练 10。
● Fundamentals of PINN: Physics-Informed Neural Networks (PINN) represent an emerging scientific machine learning approach that trains neural networks by directly embedding the physical laws governing system behavior (typically expressed as partial differential equations PDEs or ordinary differential equations ODEs) into the loss function 10.

● PINN优势：PINN的主要优势在于能够利⽤物理知识指导学习过程 ，从⽽减少对⼤量 标记数据的依赖 ，并确保模型预测结果的物理⼀致性 10。这对于许多⼯程问题 ，特别 是那些难以获取充⾜实验数据或⾼保真仿真数据的场景 ，具有重要意义。
● Advantages of PINN: The primary advantage of PINN lies in its ability to utilize physical knowledge to guide the learning process, thereby reducing reliance on large amounts of labeled data and ensuring the physical consistency of model predictions. This is particularly significant for many engineering problems, especially in scenarios where obtaining sufficient experimental data or high-fidelity simulation data is challenging.

● BNN-PINN混合模型潜⼒： 将PINN与BNN相结合 ，构建BNN-PINN混合模型 ，被认 为是解决复杂可靠性问题的⼀个有前景的⽅向 1。在这种混合模型中 ，PINN负责利⽤ 物理⽅程进⾏学习 ，⽽BNN则负责量化由于模型结构不确定性、数据噪声或物理参数 不确定性所带来的预测不确定性。1明确提出了⼀种BNN-PINN混合模型框架 ，⽤于解 决性能函数为ODE/PDE的结构可靠性问题。在电⼒驱动系统中 ，许多部件的退化过 程（如IGBT的热⽼化、 电池的电化学⽼化） 可以⽤物理或经验的微分⽅程来描述，
● Potential of BNN-PINN Hybrid Models: Combining PINN with BNN to construct BNN-PINN hybrid models is considered a promising direction for addressing complex reliability problems. In such hybrid models, PINN is responsible for learning using physical equations, while BNN quantifies the prediction uncertainties arising from model structural uncertainty, data noise, or physical parameter uncertainty. A specific BNN-PINN hybrid model framework has been proposed for solving structural reliability problems where performance functions are ODEs/PDEs. In electric drive systems, the degradation processes of many components (such as IGBT thermal aging and battery electrochemical aging) can be described by physical or empirical differential equations.

因此BNN-PINN有望在这些部件的RUL预测和可靠性评估中发挥重要作⽤ 。例如 ，10 展⽰了PINN在永磁同步电机（ PMSM） 系统故障检测中的应⽤ 10。
Therefore, BNN-PINN is expected to play an important role in the RUL prediction and reliability assessment of these components. For example, one study demonstrated the application of PINN in fault detection for permanent magnet synchronous motor (PMSM) systems.

6.3. 数据融合与混合建模⽅法
6.3. Data Fusion and Hybrid Modeling Methods

电⼒驱动系统的健康状态受多种因素影响 ，通常需要监测来⾃不同传感器的多源信息。⻉ 叶斯⽹络框架天然⽀持数据融合 ，可以将来⾃电流、 电压、温度、振动、转速等不同传感
The health status of electric drive systems is influenced by multiple factors, typically requiring monitoring of multi-source information from various sensors. The Bayesian network framework inherently supports data fusion, capable of integrating signals from diverse sensors such as current, voltage, temperature, vibration, and rotational speed.

器的数据 ，以及部件的制造信息、历史维护记录等 ，有效地融合到⼀个统⼀的模型中 ，从 ⽽做出更全⾯ 、更准确的故障诊断和预测 4。此外 ，将BN与其他机器学习模型（如⽀持向 量机SVM、随机森林RF） 或基于物理的模型进⾏混合建模 ，可以充分利⽤各⾃的优势，
the data from sensors, as well as component manufacturing information and historical maintenance records, are effectively integrated into a unified model, thereby enabling more comprehensive and accurate fault diagnosis and prediction. Furthermore, hybrid modeling approaches that combine BN with other machine learning models (such as Support Vector Machines SVM, Random Forest RF) or physics-based models can fully leverage their respective advantages,

弥补单⼀模型的不⾜ ，提⾼整体预测性能。
To compensate for the shortcomings of a single model and improve overall predictive performance.

6.4. 可解释性与模型验证挑战
6.4. Challenges of Interpretability and Model Validation

尽管BNN和PINN等⾼级模型在预测性能上展现出优势 ，但其“⿊箱”特性也带来了可解释 性的挑战。在安全关键的汽⻋应⽤中 ，理解模型为何做出特定的预测或诊断结论⾄关重 要。10指出 ，深度学习模型通常缺乏可解释性。 因此 ，发展能够解释BNN/PINN决策过程 的⽅法 ，以及建⽴针对这些⾼级模型的、被⾏业⼴泛接受的鲁棒验证和确认 (V&V)策略 , 是其成功应⽤于新能源汽⻋电⼒驱动系统可靠性保障的关键。
Although advanced models such as BNNs and PINNs demonstrate advantages in predictive performance, their "black-box" nature also presents challenges in interpretability. In safety-critical automotive applications, understanding why a model makes specific predictions or diagnostic conclusions is crucial. [10] points out that deep learning models typically lack interpretability. Therefore, developing methods to explain the decision-making processes of BNNs/PINNs, as well as establishing robust verification and validation (V&V) strategies that are widely accepted by the industry for these advanced models, is key to their successful application in ensuring the reliability of new energy vehicle power drive systems.

从BN到DBN，再到BNN以及潜在的BNN-PINN混合模型 ，这⼀系列⽅法的发展反映了⼯ 程预测领域的⼀个普遍趋势：即模型越来越能够处理⽇益增加的系统复杂性 ，适应更⼩或 含噪声的数据集 ，并提供更丰富（如概率性） 的输出。对于新能源汽⻋电⼒驱动系统⽽⾔ , 真正的技术突破可能来⾃于那些能够有效整合基于物理的理解（通过PINN或嵌⼊BN中 的显式物理⽅程） 与BNN从运⾏数据中⾃适应学习能⼒的混合模型。这种协同作⽤有望
From BNs to DBNs, then to BNNs and potential BNN-PINN hybrid models, this series of methodological developments reflects a general trend in engineering prediction: models are increasingly capable of handling growing system complexity, adapting to smaller or noisy datasets, and providing richer (e.g., probabilistic) outputs. For new energy vehicle power drive systems, true technological breakthroughs may come from hybrid models that effectively integrate physics-based understanding (through PINNs or explicit physical equations embedded in BNs) with BNNs' adaptive learning capabilities from operational data. This synergy is expected to

克服纯数据驱动或纯物理模型在⾯对新故障模式或数据稀疏情况时的局限性。
overcome the limitations of purely data-driven or purely physics-based models when facing new failure modes or data-sparse scenarios.

下表6.1⽐较了DBN、BNN和BNN-PINN混合模型在电⼒驱动系统可靠性分析中的特性。 表6.1: ⾼级贝叶斯技术 (DBN, BNN, BNN-PINN) 在电⼒驱动系统可靠性分析中的⽐较
Table 6.1 compares the characteristics of DBNs, BNNs, and BNN-PINN hybrid models in power drive system reliability analysis. Table 6.1: Comparison of Advanced Bayesian Techniques (DBN, BNN, BNN-PINN) in Power Drive System Reliability Analysis

此表为读者理解这些⾼级⽅法的具体优势及其在电⼒驱动系统动态故障预测和维护中的适 ⽤场景提供了清晰的对⽐。
This table provides readers with a clear comparison for understanding the specific advantages of these advanced methods and their applicable scenarios in dynamic fault prediction and maintenance of electric drive systems.

7. 挑战与未来研究⽅向
7. Challenges and Future Research Directions

尽管基于⻉叶斯⽹络的⽅法在新能源汽⻋电⼒驱动系统故障预测与维护⽅⾯取得了显著进 展 ，但在实际推⼴应⽤和进⼀步深化研究中仍⾯临诸多挑战。
Although Bayesian network-based methods have made significant progress in fault prediction and maintenance of new energy vehicle power drive systems, there are still numerous challenges in practical applications and further in-depth research.

7.1. 数据获取与质量问题
7.1. Data Acquisition and Quality Issues

● ⾼质量标记数据稀缺：构建精确的BN模型 ，特别是数据驱动的DBN或BNN，需要⼤ 量⾼质量的、带有故障标签的运⾏数据。然⽽ ，对于新能源汽⻋电⼒驱动系统 ，尤其 是新型号或新部件 ，这类数据往往⾮常稀缺 14。故障通常是⼩概率事件 ，积累⾜够的 故障样本需要漫⻓的时间和⼤量的⻋辆。
● Scarcity of high-quality labeled data: Constructing accurate BN models, especially data-driven DBN or BNN, requires large amounts of high-quality operational data with fault labels. However, for new energy vehicle power drive systems, particularly for new models or components, such data is often extremely scarce 14. Faults are typically low-probability events, and accumulating sufficient fault samples requires extensive time and a large number of vehicles.

● 传感器数据挑战：实际⻋辆运⾏中采集的传感器数据常常受到噪声⼲扰、存在缺失值 , 并且会因不同的驾驶习惯、道路条件和环境因素⽽表现出极⼤的变异性 21。这些问 题给数据预处理和特征提取带来了困难。
● Sensor data challenges: Sensor data collected during actual vehicle operation is often affected by noise interference, contains missing values, and exhibits significant variability due to different driving habits, road conditions, and environmental factors 21. These issues pose difficulties for data preprocessing and feature extraction.

● 数据共享与标准化：汽⻋⾏业内部的数据共享机制尚不完善 ，缺乏标准化的数据格式 和故障定义 ，这阻碍了跨⻋型、跨企业的模型开发和验证。 这个“数据挑战”是多⽅ ⾯的：不仅是数量问题 ，更关乎质量、标签的准确性、覆盖各种故障模式和⼯况的多 样性 ，以及数据的可获取性。
● Data Sharing and Standardization: The automotive industry currently lacks robust data-sharing mechanisms and standardized data formats with unified fault definitions, which hinders cross-model and cross-enterprise model development and validation. This "data challenge" is multifaceted—it concerns not only data quantity but also quality, labeling accuracy, coverage of diverse failure modes and operating conditions, as well as data accessibility.

7.2. 模型复杂性与计算效率
7.2. Model Complexity and Computational Efficiency

● 计算密集型：DBN和BNN等复杂模型的训练和推理过程可能⾮常耗时 ，特别是对于包 含⼤量节点和时间⽚的⼤规模⽹络 1。这对于需要在⻋辆上进⾏实时在线诊断或预测 的应⽤场景是⼀个主要障碍。
● Computational Intensity: Training and inference processes for complex models like DBNs and BNNs can be highly time-consuming, particularly for large-scale networks containing numerous nodes and time slices. This presents a significant obstacle for applications requiring real-time onboard diagnostics or predictions.

● 实时性要求：⻋载诊断系统 (OBD) 或预测与健康管理 (PHM) 系统往往需要在毫秒级 或秒级的时间内做出判断 ，这对模型的计算效率提出了极⾼要求。如何在保证模型精 度的前提下 ，实现轻量化和⾼效计算 ，是⼀个亟待解决的问题。
● Real-Time Requirements: On-Board Diagnostics (OBD) or Prognostics and Health Management (PHM) systems often need to make decisions within millisecond or second-level timeframes, placing extreme demands on computational efficiency. Achieving model lightweighting and efficient computation while maintaining accuracy remains a critical unsolved challenge.

7.3. 模型泛化能⼒与鲁棒性
7.3. Model Generalization Capability and Robustness

● 泛化性：在⼀个特定数据集或特定⼯况下训练得到的模型 ，能否有效地推⼴到新的、 未曾见过的⻋型、部件批次或运⾏场景 ，是衡量模型实⽤性的重要标准。
● Generalizability: Whether a model trained on a specific dataset or under specific operating conditions can be effectively extended to new, unseen vehicle types, component batches, or operational scenarios serves as a crucial criterion for evaluating the model's practical utility.

● 鲁棒性：模型需要对输⼊数据的微⼩扰动、传感器故障或⾮预期的运⾏条件具有⼀定 的抵抗能⼒ ，避免因细微变化导致预测结果的剧烈波动。10提到⼀些深度学习模型在 ⾯对变化的运⾏条件时鲁棒性不⾜。
● Robustness: The model needs to exhibit certain resistance to minor disturbances in input data, sensor failures, or unexpected operating conditions, avoiding drastic fluctuations in prediction results caused by slight variations. Reference 10 mentions that some deep learning models demonstrate insufficient robustness when facing changing operational conditions.

7.4. 标准化与⾏业应⽤
7.4. Standardization and Industry Applications

● 缺乏标准⽅法： ⽬前 ，在新能源汽⻋领域 ， 尚缺乏针对基于BN的可靠性分析和故障 预测的标准化⽅法论和⾏业基准 1。这使得不同研究成果之间的⽐较和评估变得困 难。
● Lack of standardized methods: Currently, in the field of new energy vehicles, there is still a lack of standardized methodologies and industry benchmarks for BN-based reliability analysis and fault prediction1. This makes comparison and evaluation between different research findings difficult.

● 产学研结合：如何将学术界的研究成果有效地转化为汽⻋制造和维护⾏业可以实际应 ⽤的⼯具和流程 ，仍然存在⼀定的差距 5。这需要学术界、汽⻋制造商、零部件供应 商和维护服务商之间的紧密合作。
● Industry-academia collaboration: There remains a certain gap in effectively translating academic research outcomes into practical tools and processes applicable to automotive manufacturing and maintenance industries5. This requires close cooperation among academia, automobile manufacturers, component suppliers, and maintenance service providers.

7.5. 多物理场耦合与系统级建模
7.5. Multi-physics coupling and system-level modeling

● 耦合效应： 电⼒驱动系统的故障往往不是单⼀物理场作⽤的结果 ，⽽是电⽓ 、热、机 械、化学等多物理场相互耦合、共同作⽤的产物。例如 ，IGBT的电损耗导致发热，
● Coupling effects: Failures in electric drive systems are often not the result of a single physical field's action, but rather the product of coupled interactions among multiple physical fields including electrical, thermal, mechanical, and chemical factors. For example, IGBT electrical losses lead to heating,

⾼温⼜会加速其材料⽼化和绝缘退化。未来的BN模型需要更全⾯地考虑这些耦合效 应。
High temperatures can also accelerate material aging and insulation degradation. Future BN models need to more comprehensively account for these coupling effects.

● 系统级视角： 当前的故障预测模型⼤多集中在单个部件或⼦系统层⾯ 。然⽽ ， 电⼒驱 动系统是⼀个复杂的整体 ，各部件之间存在紧密的相互依赖关系。发展能够捕捉整个 电⼒驱动系统乃⾄整⻋层⾯相互作⽤的系统级BN模型 ，对于全⾯评估⻛险和优化维 护策略⾄关重要。
● System-level perspective: Current fault prediction models predominantly focus on individual components or subsystem levels. However, electric drive systems constitute complex integrated entities with tightly interdependent relationships among components. Developing system-level Bayesian Network (BN) models capable of capturing interactions across entire electric drive systems—or even at the full vehicle level—is crucial for comprehensive risk assessment and maintenance strategy optimization.

对于新能源汽⻋电⼒驱动系统动态⻛险预测⽽⾔ ，⼀个常被低估的重⼤挑战是 ，随着新数 据的不断涌现以及⻋辆⾃⾝⽼化和出现新的或演变的故障模式 ，需要对模型进⾏持续的验 证和重新校准。这意味着汽⻋⾏业需要“活性”模型和健全的机器学习运维 (MLOps) 实
For the dynamic risk prediction of new energy vehicle power drive systems, a frequently underestimated major challenge is the need for continuous model validation and recalibration as new data continuously emerges and the vehicles themselves age, developing new or evolving failure modes. This implies that the automotive industry requires "living" models and robust Machine Learning Operations (MLOps) practices.

践。
practice.

8. 结论
8. Conclusion

8.1. 主要研究成果总结
8.1. Summary of Key Research Findings

本综述系统回顾了基于贝叶斯⽹络及其扩展模型在新能源汽⻋电⼒驱动系统动态故障⻛险
This review systematically examines the application of Bayesian networks and their extended models in dynamic fault risk assessment for new energy vehicle power drive systems

预测与维护决策⽅⾯的研究进展。主要结论如下：
Advances in predictive and maintenance decision-making research. The main conclusions are as follows:

1. 新能源汽⻋电⼒驱动系统因其复杂的⼯作条件和⾼集成度 ，对可靠性提出了严峻挑 战。传统可靠性分析⽅法（ FMEA、FTA） 在处理动态性、不确定性和多态故障⽅⾯ 存在不⾜。
1. The electric drive systems of new energy vehicles face significant reliability challenges due to their complex operating conditions and high integration levels. Traditional reliability analysis methods (FMEA, FTA) exhibit limitations in addressing dynamics, uncertainties, and multi-state faults.

2. 贝叶斯⽹络以其强⼤的概率推理、多源信息融合和不确定性处理能⼒ ，为电⼒驱动系 统的故障预测和维护提供了有效的解决思路。特别是动态贝叶斯⽹络 (DBN)在处理 时间序列数据和预测部件状态演变⽅⾯显⽰出独特优势。
2. Bayesian networks, with their powerful probabilistic reasoning, multi-source information fusion, and uncertainty handling capabilities, provide effective solutions for fault prediction and maintenance of electric drive systems. Particularly, dynamic Bayesian networks (DBNs) demonstrate unique advantages in processing time-series data and predicting component state evolution.

3. 研究表明 ，DBN已成功应⽤于电⼒驱动系统关键部件（如电机、控制器IGBT模块、 轴承等） 的故障早期预警和剩余使⽤寿命 (RUL)估计 ，为实现视情维护 (CBM)和预 测性维护 (PdM)奠定了基础。
3. Research shows that DBNs have been successfully applied to early fault warning and remaining useful life (RUL) estimation of key components in electric drive systems (such as motors, controller IGBT modules, bearings, etc.), laying the foundation for condition-based maintenance (CBM) and predictive maintenance (PdM).

4. 贝叶斯神经⽹络 (BNN)和物理信息神经⽹络 (PINN)等⾼级贝叶斯⽅法的出现 ，进⼀ 步提升了模型处理⼩样本、量化不确定性以及融合物理知识的能⼒ ，为解决电⼒驱动 系统可靠性分析中的复杂问题开辟了新途径。
4. The emergence of advanced Bayesian methods such as Bayesian neural networks (BNNs) and physics-informed neural networks (PINNs) further enhances model capabilities in handling small samples, quantifying uncertainties, and integrating physical knowledge, opening new pathways to address complex reliability analysis challenges in electric drive systems.

5. 尽管取得了显著进展 ，但基于BN的⽅法在数据获取与质量、模型复杂性与计算效 率、泛化能⼒与鲁棒性、 以及标准化与⾏业应⽤等⽅⾯仍⾯临挑战。
5. Despite significant progress, BN-based methods still face challenges in data acquisition and quality, model complexity and computational efficiency, generalization capability and robustness, as well as standardization and industrial applications.

8.2. 对未来研究的启⽰
8.2. Implications for Future Research

基于上述分析 ，未来在基于贝叶斯⽹络的新能源汽⻋电⼒驱动系统故障预测与维护领域的 研究 ，可以从以下⼏个⽅⾯重点突破：
Based on the above analysis, future research in fault prediction and maintenance of new energy vehicle power drive systems using Bayesian networks could focus on breakthroughs in the following aspects:

1. 数据驱动与机理融合的混合建模：深⼊研究如何将数据驱动的BN模型与基于物理的 失效机理模型（如通过PINN嵌⼊或作为BN的结构先验） 更紧密地结合 ，以提⾼模型 在数据稀疏或⼯况多变情况下的预测精度和鲁棒性。
1. Hybrid modeling integrating data-driven and mechanistic approaches: In-depth research on how to more closely combine data-driven BN models with physics-based failure mechanism models (such as through PINN embedding or as structural priors for BN) to improve prediction accuracy and robustness under conditions of sparse data or variable operating conditions.

2. 模型可解释性与可信度增强：开发新的技术来提⾼复杂BN模型（尤其是BNN和DBN ) 决策过程的可解释性 ，增强⽤⼾对模型预测结果的信任度 ，这对于安全关键应⽤⾄ 关重要。
2. Enhanced Model Interpretability and Trustworthiness: Develop new techniques to improve the interpretability of decision-making processes in complex BN models (particularly BNNs and DBNs), thereby increasing user confidence in model predictions—a critical requirement for safety-sensitive applications.

3. ⾼效计算与在线应⽤： 研究BN模型的压缩、近似推理算法的优化以及硬件加速技术 , 以满⾜⻋载实时故障诊断和预测的计算效率要求。
3. Efficient Computation and Online Applications: Investigate compression techniques for BN models, optimization of approximate inference algorithms, and hardware acceleration technologies to meet the computational efficiency demands of real-time vehicle fault diagnosis and prediction.

4. 多尺度、多层次的系统级建模：构建能够覆盖从元器件级到⼦系统级再到整个电⼒驱 动系统级的多层次BN模型 ，并考虑不同层级之间的动态交互和故障传播路径。
4. Multi-Scale, Multi-Level System Modeling: Construct hierarchical BN models capable of spanning from component-level to subsystem-level and ultimately to entire electric drive system-level, while accounting for dynamic interactions and fault propagation pathways across different levels.

5. 标准化数据采集与共享平台建设：推动⾏业内关于电⼒驱动系统故障数据采集、标注 和共享的标准化⼯作 ，为模型的开发、验证和⽐较提供⾼质量的数据基础。
5. Standardized Data Collection and Sharing Platforms: Promote industry-wide standardization efforts for fault data collection, annotation, and sharing in electric drive systems, thereby establishing a high-quality data foundation for model development, validation, and comparison.

6. 与数字孪⽣技术的深度集成：将BN预测模型作为新能源汽⻋数字孪⽣体的核⼼智能 模块 ，通过实时数据驱动数字孪⽣模型 ，实现对电⼒驱动系统健康状态的精准镜像、 未来趋势的精确预测以及维护决策的智能优化。这种集成有望实现对电⼒驱动系统全 ⽣命周期的闭环管理。
6. Deep integration with digital twin technology: Incorporating the BN prediction model as the core intelligent module of new energy vehicle digital twins, enabling precise mirroring of the power drive system's health status, accurate prediction of future trends, and intelligent optimization of maintenance decisions through real-time data-driven digital twin models. This integration is expected to achieve closed-loop management throughout the entire lifecycle of the power drive system.

综上所述 ，贝叶斯⽹络及其相关⾼级⽅法为新能源汽⻋电⼒驱动系统的动态故障风险预测 与维护提供了强有⼒的理论和技术⽀撑。尽管仍⾯临挑战 ，但随着研究的不断深⼊和技术
In summary, Bayesian networks and their related advanced methods provide powerful theoretical and technical support for dynamic fault risk prediction and maintenance of new energy vehicle power drive systems. Although challenges remain, with continuous research advancements and technological

的持续进步 ，基于贝叶斯⽹络的智能化故障管理系统必将在提升新能源汽车的可靠性、安 全性与经济性⽅⾯发挥越来越重要的作⽤ 。这需要学术界与⼯业界更紧密的合作 ，前者开 发先进模型 ，后者提供真实世界的数据和验证平台 ，共同推动该领域的快速发展。
progress, intelligent fault management systems based on Bayesian networks will undoubtedly play an increasingly important role in enhancing the reliability, safety, and cost-effectiveness of new energy vehicles. This requires closer collaboration between academia and industry, with the former developing advanced models and the latter providing real-world data and validation platforms, jointly driving rapid development in this field.

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