Adaptive filtering enhanced windowed correlated kurtosis for multiple faults diagnosis of locomotive bearings

Chunlin Zhang; Yuling Liu; Fangyi Wan; Binqiang Chen; Jie Liu; Bingbing Hu

doi:10.1016/j.isatra.2020.01.033

Adaptive filtering enhanced windowed correlated kurtosis for multiple faults diagnosis of locomotive bearings

ISA Trans. 2020 Jun:101:421-429. doi: 10.1016/j.isatra.2020.01.033. Epub 2020 Jan 28.

Authors

Chunlin Zhang¹, Yuling Liu², Fangyi Wan³, Binqiang Chen⁴, Jie Liu⁵, Bingbing Hu⁶

Affiliations

¹ School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China. Electronic address: zchunlin@nwpu.edu.cn.
² School of Management, Northwestern Polytechnical University, Xi'an 710072, China. Electronic address: kaixuanlin123@163.com.
³ School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China. Electronic address: fwan@nwpu.edu.cn.
⁴ School of Aerospace Engineering, Xiamen University, Xiamen 361005, China. Electronic address: cbq@xmu.edu.cn.
⁵ School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China; Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, ON K1S5B6, Canada. Electronic address: Jie.Liu@carleton.ca.
⁶ Faculty of Printing, Packing and Digital Media Engineering, Xi'an University of Technology, Xi'an 710048, China. Electronic address: hubb416@xaut.edu.cn.

PMID: 32007258
DOI: 10.1016/j.isatra.2020.01.033

Abstract

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.

Keywords: Adaptive filtering; Fault impulses isolation; Multiple faults diagnosis; Rolling bearings; Signature extraction.