Rolling bearing fault diagnosis under variable conditions using LMD-SVD and extreme learning machine

• A bearing fault diagnosis method combining LMD-SVD with ELM is proposed.
• LMD-SVD is applied to extract fault features insensitive to variable conditions.
• ELM is introduced to reduce human intervention and improve the diagnostic accuracy.

Fault diagnosis for rolling bearings under variable conditions is a hot and relatively difficult topic, thus an intelligent fault diagnosis method based on local mean decomposition (LMD)–singular value decomposition (SVD) and extreme learning machine (ELM) is proposed in this paper. LMD, a new self-adaptive time–frequency analysis method, was applied to decompose the nonlinear and non-stationary vibration signals into a series of product functions (PFs), from which instantaneous frequencies with physical significance can be obtained. Then, the singular value vectors, as the fault feature vectors, were acquired by applying SVD to the PFs. Last, for the purpose of lessening human intervention and shortening the fault-diagnosis time, ELM was introduced for identification and classification of bearing faults. From the experimental results it was concluded that the proposed method can accurately diagnose and identify different fault types of rolling bearings under variable conditions in a relatively shorter time.