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

摘要： 智能制造背景下，旋转机械工况更加复杂，运行条件更加严峻，设备的运行状态监测与故障诊断更加重要。变工况条件下，轴承振动信号存在幅值变、脉动冲击间隔、采样相位不恒定和信号噪声污染等特点，传统滚动轴承故障诊断方法的应用受到了限制。针对变工况条件下的轴承故障诊断技术，发展了以阶次跟踪、时频分析、随机振动以及混沌理论等人工提取特征的信号解调与分析方法、以卷积神经网络、自编码器与深度置信网络为代表的深度学习方法以及迁移学习方法。回顾近五年变工况轴承故障诊断领域的进展，从算法原理、算法优化以及算法实际应用等角度，详细介绍几种当前主流的变工况故障诊断方法，讨论各类算法的优势不足及适用场景，为后续的研究指明方向。

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