Review of local mean decomposition and its application in fault diagnosis of rotating machinery

• Published in: Journal of systems engineering and electronics 2019-08, Vol.30 (4), p.799-814
• Main Authors: LI Yongbo, SI Shubin, LIU Zhiliang, LIANG Xihui
• Format: Article
• Language: English
• ISSN: 1004-4132
• DOI: 10.21629/JSEE.2019.04.17

Rotating machinery is widely used in the industry. They are vulnerable to many kinds of damages especially for those working under tough and time-varying operation conditions. Early detection of these damages is important, otherwise, they may lead to large economic loss even a catastrophe. Many signal processing methods have been developed for fault diagnosis of the rotating machinery. Local mean decomposition (LMD) is an adaptive mode decomposition method that can decompose a complicated sig-nal into a series of mono-components, namely product functions (PFs). In recent years, many researchers have adopted LMD in fault detection and diagnosis of rotating machines. We give a comprehensive review of LMD in fault detection and diagnosis of rotating machines. First, the LMD is described. The advantages, disadvantages and some improved LMD methods are presented. Then, a comprehensive review on applications of LMD in fault di-agnosis of the rotating machinery is given. The review is divided into four parts: fault diagnosis of gears, fault diagnosis of rotors, fault diagnosis of bearings, and other LMD applications. In each of these four parts, a review is given to applications applying the LMD, improved LMD, and LMD-based combination methods, re-spectively. We give a summary of this review and some future potential topics at the end.