Incipient fault detection of rolling bearing using maximum autocorrelation impulse harmonic to noise deconvolution and parameter optimized fast EEMD

Incipient Fault Detection of Rolling Bearing with heavy background noise and interference harmonics is a hot topic. In this paper, a new method based on parameter optimized fast EEMD (FEEMD) and Maximum Autocorrelation Impulse Harmonic to Noise Deconvolution (MAIHND) method is proposed for detecting the incipient fault of rolling bearing. Firstly, the FEEMD method with parameters optimization is used to reduce the noise and eliminate the interference harmonics of the fault signal. As a noise assistant improved method, the FEEMD can reduce the mode mixing and enhance the calculation efficiency significantly. Secondly, a new indicator is developed to select the sensitive IMF. Finally, a novel MAIHND method is employed to extract impulse fault feature from the sensitive IMF. Simulation and experiments results indicated that the proposed parameter optimized FEEMD-MAIHND method can effectively identify the weak impulse fault feature of rolling bearing. Moreover, the excellent performance of the proposed indicator for sensitive IMF component selection and MAIHND method is verified. (C) 2018 ISA. Published by Elsevier Ltd. All rights reserved.