Abstract
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.
Highlights
Fault diagnosis of motor bearing is very significant to ensure the normal operation of the motor
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 is proposed in this paper
Experimental Results and Analysis e motor bearing data set of Case Western Reserve University is used as the experimental data. e experimental device is shown in Figure 2 [17]
Summary
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 optimizationkernel extreme learning machine (DTCWT-AFSO-KELM), is proposed in this paper. The dual tree complex wavelet transform instead of the discrete wavelet transform is used to decompose the motor bearing signal; the features with large differentiation of motor-bearing fault are extracted; the states of motor bearing are classified by using artificial fish swarm optimization-kernel extreme learning machine. E 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
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