Multi-task neural network blind deconvolution and its application to bearing fault feature extraction

Blind deconvolution (BD) is an effective method to extract fault-related characteristics from vibration signals. Previous researches focused on two primary approaches to improve the robustness and effectiveness of BD methods: developing new optimization functions or devising new methods for estimating filter coefficients. However, these methods often suffer from the difficulty of finding the global optimum due to the complex non-convex functions. To address this issue, we propose a novel multi-objective criterion, combining two well-established sparsity criteria: kurtosis and G -l1/l2 norm, that evaluates signal characteristics in both the time and frequency domains. We observe that this criterion, consisting of two sparsity criteria with opposite monotonicity, can mutually constrain and avoid overfitting that occurs with single-domain optimization. Inspired by multi-task convolutional neural networks, we introduce a multi-task 1DCNN with two branches to optimize the criterion in both domains simultaneously. To our best knowledge, it is the first time a multi-task convolutional neural network is used for BD problems. Experiments show that our method outperforms other state-of-the-art BD methods.

Automated summary

We propose a novel multi-objective criterion that combines kurtosis and G-l1/l2 norm to improve the global optimality of blind deconvolution methods. By introducing a multi-task 1DCNN with two branches, we optimize the criterion in both time and frequency domains simultaneously. Experimental results show that our method outperforms other state-of-the-art blind deconvolution methods.