Compound faults diagnosis of rolling element bearing using adaptive CYCBD and cross-correlation spectrum
ZHU Danchen1, ZHANG Yongxiang1, HE Wei2, ZHU Qunwei1
1.Naval University of Engineering, College of Power Engineering, Wuhan 430033, China;
2.Equipment Condition Detection Institution, Guangzhou Mechanical Engineering Research Institute Co., Ltd., Guangzhou 510700, China
Abstract:The separation and extraction of multi-features from the bearing compound faults signals is the key and difficult point for the identification of compound faults of rolling element bearing, especially when the signals are masked by strong background noise. Hence, a new compound faults diagnosis method is proposed in this paper based on the combination of adaptive maximum second-order cyclostationarity blind deconvolution (CYCBD) and cross-correlation spectrum. First, based on the characteristic of fault signals, various fault features are separated by using different cycle frequencies in CYCBD, the optimal filter length is determined based on the HSI; then cross-correlation is calculated to further suppress the noise; finally, fast fourier transform (FFT) is employed to acquire the cross-correlation spectrum where the fault features can be detected. Verification is performed on both simulation and experimental signals, results show that the proposed method is suitable for detecting compound faults in rolling element bearing.
朱丹宸1,张永祥1,何伟2,朱群伟1. 基于自适应CYCBD和互相关谱的滚动轴承复合故障诊断方法[J]. 振动与冲击, 2020, 39(11): 116-122.
ZHU Danchen1, ZHANG Yongxiang1, HE Wei2, ZHU Qunwei1. Compound faults diagnosis of rolling element bearing using adaptive CYCBD and cross-correlation spectrum. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(11): 116-122.
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