Zhang Chunlin, Liu Yuling, Wan Fangyi, Chen Binqiang, Liu Jie, Hu Bingbing
School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China.
School of Management, Northwestern Polytechnical University, Xi'an 710072, China.
ISA Trans. 2020 Jun;101:421-429. doi: 10.1016/j.isatra.2020.01.033. Epub 2020 Jan 28.
Compound faults diagnosis of locomotive bearings are still a challenge especially when the multi-fault impulses share the common resonant frequency. In this paper, an adaptive filtering enhanced windowed correlated kurtosis (WCK) method is proposed to isolate and identify each fault mode. A concept termed flexible analytical wavelet transform (FAWT) spectrum is defined to construct proper FAWT basis such that high signal-to-noise (SNR) is obtained in the filtered signal. Further, WCK is applied on the denoised signals to successively isolate each fault mode and determine the defects number. The performance of the proposed method is validated via analyzing experiment measurements from the locomotive bearings subjected to two local roller defects and three local outer race damages.
机车轴承的复合故障诊断仍然是一项挑战，尤其是当多个故障脉冲共享共同的共振频率时。本文提出了一种自适应滤波增强的加窗相关峭度（WCK）方法，用于分离和识别每种故障模式。定义了一种称为灵活解析小波变换（FAWT）频谱的概念，以构建合适的FAWT基，从而在滤波后的信号中获得高信噪比（SNR）。此外，将WCK应用于去噪后的信号，以依次分离每种故障模式并确定缺陷数量。通过分析来自遭受两个局部滚子缺陷和三个局部外圈损伤的机车轴承的实验测量数据，验证了所提方法的性能。
