Energy-Fluctuated Multiscale Feature Learning With Deep ConvNet for Intelligent Spindle Bearing Fault Diagnosis

Abstract

Considering various health conditions under varying operational conditions, the mining sensitive feature from the measured signals is still a great challenge for intelligent fault diagnosis of spindle bearings. This paper proposed a novel energy-fluctuated multiscale feature mining approach based on wavelet packet energy (WPE) image and deep convolutional network (ConvNet) for spindle bearing fault diagnosis. Different from the vector characteristics applied in intelligent diagnosis of spindle bearings, wavelet packet transform is first combined with phase space reconstruction to rebuild a 2-D WPE image of the frequency subspaces. This special image can reconstruct the local relationship of the WP nodes and hold the energy fluctuation of the measured signal. Then, the identifiable characteristics can be further learned by a special architecture of the deep ConvNet. Other than the traditional neural network architecture, to maintain the global and local information simultaneously, deep ConvNet combines the skipping layer with the last convolutional layer as the input of the multiscale layer. The comparisons of clustering distribution and classification accuracy with six other features show that the proposed feature mining approach is quite suitable for spindle bearing fault diagnosis with multiclass classification regardless of the load fluctuation.