Complete ensemble local mean decomposition with adaptive noise and its application to fault diagnosis for rolling bearings

• Published in: Mechanical systems and signal processing Vol. 106; pp. 24 - 39
• Main Authors: Wang, Lei, Liu, Zhiwen, Miao, Qiang, Zhang, Xin
• Format: Journal Article
• Language: English
• Published: Berlin Elsevier Ltd 01.06.2018; Elsevier BV
• Subjects: Complete ensemble local mean decomposition with adaptive noise (CELMDAN); Decomposition; Ensemble local mean decomposition (ELMD); Fault diagnosis; Noise; Roller bearings; Rolling bearing; Vibration; White noise; Fault diagnosis; Ensemble local mean decomposition (ELMD); Rolling bearing; Complete ensemble local mean decomposition with adaptive noise (CELMDAN)
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2017.12.031

•This paper proposes a novel method named CELMDAN.•The proposed method overcomes the drawbacks of ELMD.•The signal processing performance of CELMDAN is analyzed comparatively.•Simulations and applications show the validity and superiority of CELMDAN. Mode mixing resulting from intermittent signals is an annoying problem associated with the local mean decomposition (LMD) method. Based on noise-assisted approach, ensemble local mean decomposition (ELMD) method alleviates the mode mixing issue of LMD to some degree. However, the product functions (PFs) produced by ELMD often contain considerable residual noise, and thus a relatively large number of ensemble trials are required to eliminate the residual noise. Furthermore, since different realizations of Gaussian white noise are added to the original signal, different trials may generate different number of PFs, making it difficult to take ensemble mean. In this paper, a novel method is proposed called complete ensemble local mean decomposition with adaptive noise (CELMDAN) to solve these two problems. The method adds a particular and adaptive noise at every decomposition stage for each trial. Moreover, a unique residue is obtained after separating each PF, and the obtained residue is used as input for the next stage. Two simulated signals are analyzed to illustrate the advantages of CELMDAN in comparison to ELMD and CEEMDAN. To further demonstrate the efficiency of CELMDAN, the method is applied to diagnose faults for rolling bearings in an experimental case and an engineering case. The diagnosis results indicate that CELMDAN can extract more fault characteristic information with less interference than ELMD.