电机轴承智能故障诊断综述

Review on Intelligent Fault Diagnosis of Motor Bearings

智能故障诊断技术(Intelligent Fault Diagnosis,IFD)将深度学习理论应用于设备故障诊断,能够自动识别电机轴承的健康状态和故障类型,在设备故障诊断领域引起了广泛关注。驱动电机是电动车辆的动力来源,其功率、扭矩和响应速度等性能参数决定了电动车辆的加速性能、最高速度和操控性能。轴承作为电机的关键部件,任何微小的轴承故障都会对车辆的安全性和可靠性产生不利影响,因此对轴承进行故障诊断至关重要。介绍了电机轴承故障的原因、信号处理方法和传统机器学习方法,并重点从深度学习方面对滚动轴承故障诊断经典算法和应用进行了综述,总结了不同方法的优点、局限性和应用现状,并对深度学习在故障诊断领域的未来发展方向进行了展望。

Intelligent fault diagnosis(IFD)technology applies deep learning theory to equipment fault diagnosis,which can automatically identify the health status and fault type of motor bearings,and has attracted wide attention in the field of equipment fault diagnosis.The driving motor is the core power source of electric vehicle.Its power,torque,response speed and other performance parameters directly affect the acceleration performance,maximum speed and control performance of electric vehicle.As a key part of the motor,any small fault of the bearing may have an adverse impact on the safety and reliability of the vehicle,so the fault diagnosis of the bearing is very important.The causes of motor bearing failure,signal processing methods and traditional machine learning methods are introduced.Especially in the aspect of deep learning,the classical algorithms and applications of rolling bearing fault diagnosis are reviewed.The advantages,limitations and application status of different methods are also summarized,and the future development direction of deep learning in fault diagnosis field is prospected.