基于时序关联分析的旋转机械故障诊断

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振动与冲击 ›› 2022, Vol. 41 ›› Issue (8) : 171-178.

论文

基于时序关联分析的旋转机械故障诊断

谭帅1，马遥1，侍洪波1，常玉清2，郭磊1

作者信息 +

Fault diagnosis of rotating machinery based on time-series correlation analysis

TAN Shuai1,MA Yao1,SHI Hongbo1,CHANG Yuqing2,GUO Lei1

Author information +

文章历史 +

摘要

随着大型旋转机械设备的广泛应用，高速旋转机械的故障诊断得到越来越多的关注。旋转机械的周期旋转特性导致信号间存在很强的时序关联关系，当故障发生时，故障特性会在旋转周期间逐渐传递。文章分析滚动轴承不同类型故障、不同损伤程度振动信号时序相关特性的差异度，提出了周期记忆神经网络(Periodization long short-term memory,P-LSTM)故障诊断方法。该方法首先提取旋转机械周期内数据特征，并利用记忆因子对特性在周期间的传递规律进行选择性遗忘，学习其周期间的时序相关特征，从而实现滚动轴承的故障诊断。最后利用滚动轴承多类故障数据对所提出方法进行性能分析和试验，验证了P-LSTM方法学习旋转机械周期间的时序相关特性的有效性，以及进行故障诊断的准确度。

Abstract

With the application of large-scale rotating machinery, more and more attention has been paid to fault diagnosis of high-speed rotating machinery. Due to the periodic rotation characteristics of rotating machinery, there is a strong temporal correlation between signals. Fault features will gradually transfer during the week of rotation. In this paper, different types of faults and different level of damages of rotating machinery were analysed, which refer to temporal association characteristics of vibration signal. Then Periodization long short-term memory (Periodization – LSTM,P-LSTM) fault diagnosis method was proposed. The method extract features from periodization data and use memory factors to forget some information that of insignificance. Finally, the performance analysis and test of the proposed method were carried out based on the multi-fault data of rolling bearings, which verified the effectiveness of p-lstm method in learning the time-series correlation characteristics of rotating machinery during the cycle, as well as the accuracy of fault diagnosis.

导出引用

谭帅1，马遥1，侍洪波1，常玉清2，郭磊1. 基于时序关联分析的旋转机械故障诊断[J]. 振动与冲击, 2022, 41(8): 171-178

TAN Shuai1,MA Yao1,SHI Hongbo1,CHANG Yuqing2,GUO Lei1. Fault diagnosis of rotating machinery based on time-series correlation analysis[J]. Journal of Vibration and Shock, 2022, 41(8): 171-178

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