Multiscale Weighted Morphological Network Based Feature Learning of Vibration Signals for Machinery Fault Diagnosis

Vibration signals are widely utilized for machinery fault diagnosis. However, the fault-related components (i.e., impulse) in vibration signals are often buried by strong background noises due to complex working conditions. Thus, it is challenging to directly extract discriminate features from vibration signals. In this article, a novel deep neural network (DNN), multiscale weighted morphological network (MWMNet), is proposed to extract impulses from vibration signals. First, a novel morphological layer is smoothly embedded in DNN as a signal processing layer to extract impulses and filter out the noise. Second, multiple branches with different structure element (SE) scales are employed to respectively extract impulses. An adaptive weighted fusion is utilized to enhance the scales that provide strong impulsive components. Third, a morphological operator, called average-hat transform, is adopted in MWMNet, and both positive and negative impulses can be extracted from vibration signals. The effectiveness of MWMNet is validated by the experiments of gearbox fault diagnosis and bearing fault diagnosis. The experimental results show that MWMNet can learn the fault-related features and filter out noise from vibration signals well. The comparison results illustrate that MWMNet has a better fault diagnosis performance than those DNNs, e.g., one-dimensional convolutional neural network, densely connected convolutional network, and residual network.