Liao, J.; Dong, H.; Luo, L.; Sun, J.; Zhang, S. Multi-Task Neural Network Blind Deconvolution and Its Application to Bearing Fault Feature Extraction. Measurement Science and Technology 2023, doi:10.1088/1361-6501/accbdb.
Liao, J.; Dong, H.; Luo, L.; Sun, J.; Zhang, S. Multi-Task Neural Network Blind Deconvolution and Its Application to Bearing Fault Feature Extraction. Measurement Science and Technology 2023, doi:10.1088/1361-6501/accbdb.
Liao, J.; Dong, H.; Luo, L.; Sun, J.; Zhang, S. Multi-Task Neural Network Blind Deconvolution and Its Application to Bearing Fault Feature Extraction. Measurement Science and Technology 2023, doi:10.1088/1361-6501/accbdb.
Liao, J.; Dong, H.; Luo, L.; Sun, J.; Zhang, S. Multi-Task Neural Network Blind Deconvolution and Its Application to Bearing Fault Feature Extraction. Measurement Science and Technology 2023, doi:10.1088/1361-6501/accbdb.
Abstract
Blind deconvolution (BD) is one of the effective methods that help pre-process vibration signals and assist in bearing fault diagnosis. Currently, most BD methods design an optimization criterion and use frequency or time domain information independently to optimize a deconvolution filter. It recovers weak periodic impulses related to incipient faults. However, the random noise interference may cause the optimizer to overfit. The time-domain-based BD methods tend to extract fault-unrelated single peak impulse, and the frequency-domain-based BD methods tend to retain the maximum energy frequency component, which will lose the fault-related harmonics frequency components. To solve the above issue, we propose a hybrid criterion that combines the kurtosis for time domain optimization and the $G-l_1/l_2$ norm for the frequency domain. These two criteria are monotonically increasing and decreasing, so they mutually constrain to avoid overfitting. After that, we design a multi-task one-dimensional convolutional neural network with time and frequency branches to achieve an optimal solution for this hybrid criterion. The multi-task neural network realizes the simultaneous optimization of two domains. Experimental results show that our proposed method outperforms other state-of-the-art methods.
Copyright:
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