基于分层算子形态小波的轮对轴承复合故障检测  

Wheelset-bearing Compound Fault Detection Based on Layered-operator Morphological Wavelet

作　　者：李奕璠 杨杰[1] 陈再刚[2] 易彩 林建辉[2] LI Yifan;YANG Jie;CHEN Zaigang;YI Cai;LIN Jianhui(School of Mechanical Engineering,Southwest Jiaotong University,Chengdu 610031;State Key Laboratory of Traction Power,Southwest Jiaotong University,Chengdu 610031)

机构地区：[1]西南交通大学机械工程学院,成都610031 [2]西南交通大学牵引动力国家重点实验室,成都610031

出　　处：《机械工程学报》2022年第10期1-11,共11页Journal of Mechanical Engineering

基　　金：国家自然科学基金(52072321,52022083);牵引动力国家重点实验室自主课题(2021TPL-T04)资助项目。

摘　　要：形态学算子可以分为降噪型算子和特征提取型算子两大类。现有的形态非抽样小波方法在多层分解的每一层均使用相同的形态学算子,但反复使用某种降噪型或特征提取型算子有时很难准确获得信号的特征信息。为此,提出分层算子形态非抽样小波,每一层分解采用不同的形态学算子,通过对去噪型和特征提取型算子的有机融合,方法在故障特征提取方面具有更强的针对性、灵活性,同时也具有明确的物理意义和可解释性。针对轮对轴承复合故障的特点,提出一种局部特征幅值比原则,从不同分辨率对应的不同分析尺度的分析结果中,分别挑选出对不同类型故障最为敏感的分析尺度,进而实现复合故障中各故障的有效分离。在试验台采集轮对轴承复合故障振动信号,将提出的分层算子形态小波应用于实测数据的分析。研究结果表明,提出的方法能有效检测轮对轴承复合故障,与现有的形态非抽样小波方法相比,分层算子形态小波对复合故障的辨识能力更强。Morphological operators are divided into two categories:noise reduction operators and feature extraction operators. The reported morphological undecimated wavelets use the identical morphological operator in each decomposition level, but it is challenging to capture the characteristic information of a signal simply by using a noise reduction or feature extraction operator repeatedly. Therefore, a layered-operator morphological undecimated wavelet is proposed in the paper, and different morphological operators are employed for each level of decomposition. The proposed method is more targeted and flexible in bearing fault feature extraction through the combination of noise reduction and feature extraction operators and with clear physical significance and easy to interpret. According to the characteristics of wheelset-bearing compound faults, a local characteristic amplitude ratio principle is proposed to select the most sensitive scale for each type of fault from multiple filtering scales to separate each fault effectively. The wheelset-bearing compound fault vibration signals are collected on a test rig, and the proposed layered operator morphological wavelet is applied to process the measured data. The results show that the proposed method can effectively detect the compound faults of wheelset bearings. Compared with the reported morphological undecimated wavelets, the layered operator morphological wavelet presents a superior performance in identifying the compound faults.

关 键 词：形态非抽样小波 形态滤波 故障诊断 复合故障 轮对轴承 

分 类 号：TH17[机械工程—机械制造及自动化]