Aiming at the problems of data transmission, storage, and processing difficulties in the fault diagnosis of bearing acoustic emission (AE) signals, this paper proposes a weak fault feature enhancement diagnosis method for processing bearing AE signals in the compressed domain based on the theory of compressed sensing (CS). This method is based on the frequency band selection scheme of CS and particle swarm optimization (PSO) method. Firstly, the method uses CS technology to compress and sample the bearing AE signal to obtain the compressed signal; then, the compressed AE signals are decomposed by the compression domain wavelet packet decomposition matrix to extract the characteristic parameters of different frequency bands, and then the weighted sum of the characteristic parameters is carried out. At the same time, the PSO method is used to optimize the weight coefficient to obtain the enhanced fault characteristics; finally, a feature-enhanced-support vector machine (SVM) fault diagnosis model is established. Different feature parameters are feature-enhanced to form a feature set, which is used as input, and the SVM method is used for pattern recognition of different types and degrees of bearing faults. The experimental results show that the proposed method can effectively extract the fault features in the bearing AE signal while improving the efficiency of signal processing and analysis and realize the accurate classification of bearing faults.
Citation: Cong Wang, Chang Liu, Mengliang Liao, Qi Yang. An enhanced diagnosis method for weak fault features of bearing acoustic emission signal based on compressed sensing[J]. Mathematical Biosciences and Engineering, 2021, 18(2): 1670-1688. doi: 10.3934/mbe.2021086
Aiming at the problems of data transmission, storage, and processing difficulties in the fault diagnosis of bearing acoustic emission (AE) signals, this paper proposes a weak fault feature enhancement diagnosis method for processing bearing AE signals in the compressed domain based on the theory of compressed sensing (CS). This method is based on the frequency band selection scheme of CS and particle swarm optimization (PSO) method. Firstly, the method uses CS technology to compress and sample the bearing AE signal to obtain the compressed signal; then, the compressed AE signals are decomposed by the compression domain wavelet packet decomposition matrix to extract the characteristic parameters of different frequency bands, and then the weighted sum of the characteristic parameters is carried out. At the same time, the PSO method is used to optimize the weight coefficient to obtain the enhanced fault characteristics; finally, a feature-enhanced-support vector machine (SVM) fault diagnosis model is established. Different feature parameters are feature-enhanced to form a feature set, which is used as input, and the SVM method is used for pattern recognition of different types and degrees of bearing faults. The experimental results show that the proposed method can effectively extract the fault features in the bearing AE signal while improving the efficiency of signal processing and analysis and realize the accurate classification of bearing faults.
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Cong Wang, Chang Liu, Mengliang Liao, Qi Yang. An enhanced diagnosis method for weak fault features of bearing acoustic emission signal based on compressed sensing[J]. Mathematical Biosciences and Engineering, 2021, 18(2): 1670-1688. doi: 10.3934/mbe.2021086
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