Deep network-based maximum correlated kurtosis deconvolution: A novel deep deconvolution for bearing fault diagnosis

Deconvolution methods (DMs) which can adaptively design the filter for the feature extraction is the most effective tool to counteract the effect of the transmission path. Convolutional sparse filter (CSF) as a new deconvolution mode, which transfers the complicated numeric calculation to the simple feature learning for the optimization and solution of the deconvolution filter coefficient using neural network, has a remarkable superiority especially under the heavy noise condition compared with the traditional DMs. Yet, the problems of the filter length selection and the sensibility to random interference largely confine its application. Motived by this, a novel deep network-based maximum correlated kurtosis deconvolution (MCKD-DeNet) is proposed in this paper. Firstly, according to the multiple-inputs way of the neural network, a filter initialization is designed using the Hanning window. With different filters guided by the initialization, a serial of filtered signals is input to learn the fault feature. Secondly, correlated kurtosis, which can simultaneously evaluate the periodicity and impulsiveness of the signal, is chosen as the new cost function to train the neural network. And the input period is estimated between the layers by calculating the autocorrelation of the most informative filtered signal. Subsequently, the component with most fault information is locked as the output of MCKD-DeNet using the proposed dimension reduction method based on the correlation coefficient. Finally, compared with previous CSF and improved maximum correlated kurtosis deconvolution, the proposed MCKD-DeNet is verified to have the performance superiority by simulated signal with different noise levels and interference as well as experimental data collected from wind turbine experiment bench with bearing fault.