Special Session SS01

Operational Reliability Assessment and Enhancement of Electrical Machine System

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Introduction:

With the accelerating electrification of transportation, industry, defense, and energy systems, electric machines have become critical components in a wide range of mission-critical applications. Unlike conventional drive systems operating under relatively steady conditions, modern electric machines are increasingly exposed to complex operating conditions, including frequent start–stop cycles, wide speed variations, highly dynamic loads, and harsh environmental stresses such as high temperature, high humidity, salt spray, and vibration. These factors can accelerate the degradation of critical components, including stator insulation, bearings, and permanent magnets, thereby increasing the risk of unexpected failures, costly downtime, and even catastrophic safety incidents.
To improve the accuracy and effectiveness of reliability assessment and health-state monitoring for electric machine systems, advanced approaches such as multiphysics coupled modeling and data-driven fusion are required to elucidate degradation mechanisms and identify the underlying causes of failure evolution. Meanwhile, nonintrusive condition monitoring, lifetime prediction, and fault-tolerant control can further enhance the reliability assurance of electric machine systems.
This Special Issue focuses on the operation reliability assurance and enhancement of electric machine systems under complex operating conditions. It aims to explore new theories, methodologies, and technologies for enabling sustained high-performance and highly reliable operation of electric machines throughout their entire life cycle.
The topics of interest include, but are not limited to:
1. Damage accumulation methods and RUL prediction under complex operating conditions;
2. Degradation modeling and failure mechanism analysis of motor insulation;
3. Signal identification and feature extraction for early fault detection in electric machines;
4. Reliability assessment and analysis of electric machine systems;
5. Health management and condition assessment of electric machine systems;
6. Multistress accelerated testing methods for electric machine components;
7. Reliability-oriented design of electric machines;
8. AI-enabled reliability enhancement of electric machine systems.

SS01's Session Chair:

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Prof. Feng Niu, Hebei University of Technolog, China

SS01's Session Co-chairs:

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Senior Research Fellow Yifu Ren, Tsinghua University, China

Lecturer Xuanming Zhou, Hebei University of Technology, China;