A Fault Diagnosis Method of Train Wheelset Rolling Bearing Combined with Improved LMD and FK. (4th December 2019)

• Record Type: Journal Article
• Authors: Zhang, Yu; Fan, Zhuoyou; Gao, Xiaorong; Luo, Lin
• Other Names: Potirakis Stelios M. Academic Editor.
• Is Part Of: Journal of sensors. Volume 2019(2019)
• Publication Date: 2019-12-04
• Subjects: Detectors -- Periodicals; 681.205
• DOI: 10.1155/2019/6207847
• Languages: English
• ISSNs: 1687-725X

Abstract

Abstract : Trackside acoustic signals contain intense noise and nonstationary features even after Doppler distortion correction. Information on bearing defects in these signals is either weak or heavily attenuated. Thus, an improved compound interpolation envelope local mean decomposition (ICIE LMD) method combined with a fast kurtogram (FK) is proposed for wheelset bearings. In this methodology, cubic Hermite interpolation and cubic spline interpolation are employed to find the envelope of the extremal points in the ICIE LMD algorithm to improve accuracy and decrease the computing time of the decomposed signal component. An FK is sensitive to the impact signal and extracts the fault impact features efficiently. In the application, the proposed method uses ICIE LMD to decompose the multicomponent signal into several specific single product function (PF) components. The kurtosis index of the PF is calculated to select the component which contains the most fault information. Then, the selected component of PF is filtered by FK. Finally, the squared envelope spectrum is used to obtain the fault frequency and identify the fault location. The advantages of the ICIE LMD method are verified by simulation analysis. In the application, the results show that the proposed method efficiently extracts the fault features and enhances the target characteristics of the sound signals from a trackside microphone array. Furthermore, the influence of rotating frequency on locating the fault is reduced.