基于同步挤压S变换和集成深层脊波自编码器的轴承故障诊断

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振动与冲击 ›› 2020, Vol. 39 ›› Issue (14) : 59-68.

论文

杜小磊1,2，陈志刚1,2，王衍学1,2

作者信息 +

Bearing fault diagnosis based on the synchrosqueezed S transform and ensemble deep ridgelet auto-encoder

DU Xiaolei1,2，CHEN Zhigang1,2，WANG Yanxue1,2

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文章历史 +

摘要

针对传统滚动轴承故障诊断算法过度依赖专家经验和故障特征提取困难的问题，提出一种基于同步挤压S变换（Synchrosqueezed S transform，SSST）和集成深层脊波自编码器（Ensemble deep ridgelet auto-encoder，EDRAE）方法。该方法首先对轴承振动信号进行SSST变换得到时频图像，并将时频图像进行双向二维主成分分析压缩；其次，利用不同的脊波函数设计不同的脊波自编码器（Ridgelet auto-encoder，RAE），并构造相应的深层脊波自编码器（Deep ridgelet auto-encoder，DRAE）且引入“跨层”连接以缓解DRAE的梯度消失现象；最后将压缩时频图像输入各DRAE网络进行无监督预训练和有监督微调，并通过加权平均法输出识别结果。实验结果表明，基于SSST和EDRAE的轴承故障诊断方法能有效地对轴承进行多种工况和多种故障程度的识别，特征提取能力和识别能力均优于人工神经网络、深度信念网络和深度自编码器等模型。

Abstract

Aiming at the problems of traditional fault diagnosis algorithms of rolling bearings having such shortcomings as largely dependent on expert prior knowledge and difficulty in fault feature extraction, a method based on synchrosqueezed S transform (SSST) and ensemble deep ridgelet auto-encoder (EDRAE) was proposed. Firstly, the vibration signals were transformed by SSST to get time-frequency images. Then time-frequency images were compressed by two-directional two dimensional principal components analysis (TD-2DPCA). Secondly, different ridgelet auto-encoders (RAEs) with different ridgelet functions were designed. Then, different deep ridgelet auto-encoders (DRAEs) with different RAEs were designed and a "cross-layer" connection was introduced to alleviate gradient disappearance of DRAEs. Finally, the compressed images were input into each DRAE for unsupervised pre-training and supervised fine-tuning, and the recognition result was given by the weighted averaging method. Experimental results show that the proposed method can effectively identify the bearing faults under multiple working conditions and multiple fault severities. The proposed method has better ability of feature extraction and recognition than artificial neural network, deep belief network, deep auto-encoder and so on.

导出引用

杜小磊1,2，陈志刚1,2，王衍学1,2. 基于同步挤压S变换和集成深层脊波自编码器的轴承故障诊断[J]. 振动与冲击, 2020, 39(14): 59-68

DU Xiaolei1,2，CHEN Zhigang1,2，WANG Yanxue1,2. Bearing fault diagnosis based on the synchrosqueezed S transform and ensemble deep ridgelet auto-encoder[J]. Journal of Vibration and Shock, 2020, 39(14): 59-68

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