关键词: 变工况, 故障诊断, 深度学习, 迁移学习, 时频分析, 阶次跟踪

Abstract: The working conditions of rotating machinery are more compound and the operating conditions are more severe in the context of intelligent manufacturing, leading to more substantial monitoring and fault diagnosis of the operating conditions of the equipment. Under the variable working conditions, the bearing vibration signal has the characteristics of amplitude variation, pulsating impact interval, non-constant sampling phase and signal noise pollution, etc, which limits the application of traditional rolling bearing fault diagnosis methods. For bearing fault diagnosis technology under variable working conditions, signal demodulation and analysis methods with artificially extracted features such as order tracking, time-frequency analysis, random vibration and chaos theory, deep learning methods represented by convolutional neural networks, self-encoder and deep confidence networks, and transfer learning methods have been developed. This paper reviews the progress in the field of variable condition bearing fault diagnosis in the past five years, introduces several current mainstream variable condition fault diagnosis methods in detail from the perspectives of algorithm principle, algorithm optimization and practical application of algorithms, discusses the advantages and shortcomings of various algorithms and their application scenarios, and points out the direction for the subsequent research.

Key words: variable working conditions, fault diagnosis, deep learning, transfer learning, time-frequency analysis, order tracking