A parameter-adaptive ACMD method based on particle swarm optimization algorithm for rolling bearing fault diagnosis under variable speed

• DOI: 10.1007/s12206-021-0405-7
• Publication Date: 2021-04-20T05:03:11Z
• Authors: Zengqiang Ma; Feiyu Lu; Suyan Liu; Xin Li
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

Fault diagnosis for rolling bearing under variable speed is always a challenging topic since the vibration signal has time-varying characteristics. To overcome this difficulty, a novel method is exploited based on particle swarm optimization (PSO) and adaptive chirp mode decomposition (ACMD), named parameter-adaptive ACMD. Firstly, fast spectral kurtosis algorithm is used to get the resonance band signal. Then, the parameter-adaptive ACMD method decomposes the envelope signal to obtain the time-frequency spectrum. Next, the proposed method of fault diagnosis uses peak search algorithm to estimate instantaneous rotational frequency from the time-frequency graph processed. Finally, the measured rotational frequency is used as the phase function in the resampling process to get the order spectrum and fault characteristic order (FCO). Simulation and actual signals were analyzed and the results indicate that the proposed method can identify fault type with variable speed and has high application value.