A rotating machinery fault diagnosis method based on local mean decomposition

• Published in: Digital signal processing Vol. 22; no. 2; pp. 356 - 366
• Main Authors: Cheng, Junsheng, Yang, Yi, Yang, Yu
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.03.2012
• Subjects: Decomposition; Fault diagnosis; Faults; Gears; Local mean decomposition; Modulation; Product functions; Roller bearings; Rotating machinery; Time-frequency analysis; Vibration; Fault diagnosis; Local mean decomposition; Product functions; Time–frequency analysis; Rotating machinery; Modulation
• ISSN: 1051-2004; 1095-4333
• DOI: 10.1016/j.dsp.2011.09.008

Local mean decomposition (LMD) is a novel self-adaptive time–frequency analysis method, which is particularly suitable for the processing of multi-component amplitude-modulated and frequency-modulated (AM–FM) signals. By using LMD, any complicated signal can be decomposed into a number of product functions (PFs), each of which is the product of an envelope signal and a purely frequency modulated signal from which physically meaningful instantaneous frequencies can be obtained. In fact, each PF is just a mono-component AM–FM signal. Therefore, the procedure of LMD may be regarded as the process of demodulation. While fault occurs in gear or roller bearing, the vibration signals picked up would exactly display AM–FM characteristics. So it is possible to diagnose gear and roller bearing fault by LMD. Targeting the modulation features of the gear or roller bearing fault vibration signal, a rotating machinery fault diagnosis method based on LMD is proposed. In this paper, firstly the LMD method is introduced; secondly, the LMD method is compared with another competing time–frequency analysis approach, namely, empirical mode decomposition (EMD) method and the results show the superiority of the LMD method; finally, the LMD method is applied to the gear and roller bearing fault diagnosis. The analysis results from the practical gearbox vibration signal demonstrate that the diagnosis approach based on LMD could identify gear and roller bearing work condition accurately and effectively.