Abstract

Fault diagnosis plays a vital role in prognostics and health management. Researchers have devoted their efforts in enhancing the accuracy of fault diagnosis. However, diagnosis of compound faults in complex systems is still a challenging task. The problem lies in the coupling of multiple signals, which may conceal the characteristics of compound faults. Taking a rolling bearing as an example, this study aims to boost the accuracy of compound fault diagnosis through a novel feature extraction approach to making the fault characteristics more discriminative. The approach proposes an adaptive dual-tree complex wavelet packet transform (DTCWPT) with higher order spectra analysis. To flexibly and best match the characteristics of the measured vibration signals under analysis, DTCWPT is first adaptively determined by the minimum singular value decomposition entropy. Then, higher order spectra analysis is performed on the decomposed frequency sensitive band for feature extraction and enhancement. The proposed approach is used to analyze experimental signals of a bearing’s compound faults and found effective.

摘要

故障诊断在预测和健康管理中起着至关重要的作用。研究人员致力于提高故障诊断的准确性。但是，诊断复杂系统中的复合故障仍然是一项艰巨的任务。问题在于多个信号的耦合，这可能掩盖复合故障的特征。以滚动轴承为例，本研究旨在通过一种新颖的特征提取方法来提高复合故障诊断的准确性，从而使故障特征更具区分性。该方法提出了具有更高阶频谱分析的自适应双树复数小波包变换（DTCWPT）。为了灵活，最佳地匹配所分析振动信号的特性，首先由最小奇异值分解熵自适应地确定DTCWPT。然后，对分解后的敏感频段执行更高阶的频谱分析，以进行特征提取和增强。所提出的方法用于分析轴承的复合故障的实验信号，并发现是有效的。