Deep residual learning with demodulated time-frequency features for fault diagnosis of planetary gearbox under nonstationary running conditions
Deep residual learning with demodulated time-frequency features for fault diagnosis of planetary gearbox under nonstationary running conditions
复制标题
具有解调时频特征的深度残差学习用于非平稳运行条件下行星齿轮箱的故障诊断
DOI：
10.1016/j.ymssp.2019.02.055
复制
发表时间：
2019-07-15
影响因子：
8.4
通讯作者：
Han, Qinkai
中科院分区：
文献类型：
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作者：
Ma, Sai;Chu, Fulei;Han, Qinkai
由于旋转机械传动链系统工作环境的坚韧性和时变性，其故障诊断技术具有重要意义。近年来，许多统计和光谱特征提取方法已被开发和应用，但不幸的是，他们无法处理在不同的运行条件下的力学行为。此外，缺乏具体的动力学知识也成为一个障碍，有效地诊断通过直接频谱分析。因此，本文提出了一种基于时频分析和深度残差网络的数据驱动的故障诊断方法。首先，在固定转速下提取的光谱特征上预训练深度残差网络。对于瞬态信号，采用基于时频表示的概率瞬时角速度估计算法构造精确的相位函数。然后利用广义解调算子消除转速波动。然后，将从时频表示中解调出的多组瞬时特征输入到深度残差网络中，测试该方法在非平稳运行条件下的性能。以某行星齿轮箱试验台为例，将诊断结果与传统方法进行了比较，结果表明，所提出的数据驱动的故障诊断方法在变转速条件下，早期故障检测精度有了显著提高。(C)2019爱思唯尔有限公司版权所有。
Due to the tough and time-varying working conditions, fault diagnosis technique is of critical significance for drive-chain system in rotating machines. In recent years, many statistical and spectral feature extraction methods have been developed and applied, but unfortunately, they are incapable of dealing with mechanical behaviors under varying running conditions. Besides, the lack of specific dynamical knowledge also becomes an obstacle for effective diagnosis through direct spectral analysis. Accordingly, a data-driven fault diagnosis method based on time-frequency analysis and deep residual network is proposed in this research. Firstly, a deep residual network is pre-trained on spectral features extracted under fixed rotating speeds. For the transient signals, an accurate phase function is constructed via probabilistic instantaneous angular speed (IAS) estimation algorithm based on time-frequency representations. Then the generalized demodulation operator is utilized to remove rotating speed fluctuation. Afterwards, several groups of instantaneous features demodulated from time-frequency representations are input to the deep residual network to test the performance of proposed method under nonstationary running conditions. The diagnosis results of a planetary gearbox test rig are compared with other traditional methods; the comparisons show that the proposed data-driven fault diagnosis method achieved significant improvement on incipient fault detection accuracy under varying rotating speed. (C) 2019 Elsevier Ltd. All rights reserved.