A Novel Fault Diagnosis Method for Motor Bearing Based on DTCWT and AFSO‐KELM
Yan Lu, Peijiang Li · Shock and Vibration · 2021
Aiming at the defects of wavelet transform‐based feature extraction and extreme learning machine‐based classification, a novel fault diagnosis method for motor bearing, based on dual tree complex wavelet transform and artificial fish swarm optimization‐kernel extreme learning machine (DTCWT‐AFSO‐KELM), is proposed in this paper. Firstly, the dual tree complex wavelet transform instead of the discrete wavelet transform is used to decompose the motor bearing signal; then, the features with large differentiation of motor‐bearing fault are extracted; finally, the states of motor bearing are classified by using artificial fish swarm optimization‐kernel extreme learning machine. In order to better prove the superiority of this method, four kinds of state data of motor bearing under the conditions of 0 HP (horsepower) load, 1 HP load, 2 HP load, and 3 HP load are used to test. The experimental results indicate that the diagnosis accuracies of DTCWT‐AFSO‐KELM are obviously better than those of discrete wavelet transform and artificial fish swarm optimization‐kernel extreme learning machine (DWT‐AFSO‐KELM) or discrete wavelet transform and extreme learning machine (DWT‐ELM) under different loads.