A Multi-size Kernel based Adaptive Convolutional Neural Network for Bearing Fault Diagnosis

Abstract

Bearing fault identification and analysis is an important research area in the field of machinery fault diagnosis. Aiming at the common faults of rolling bearings, we propose a datadriven diagnostic algorithm based on the characteristics of bearing vibrations called multisize kernel based adaptive convolutional neural network (MSKACNN). Using raw bearing vibration signals as the inputs, MSKACNN provides vibration feature learning and signal classification capabilities to identify and analyze bearing faults. Ball mixing is a ball bearing production quality problem that is difficult to identify using traditional frequency domain analysis methods since it requires high frequency resolutions of the measurement signals and results in a long analyzing time. The proposed MSKACNN is shown to improve the efficiency and accuracy of ball mixing diagnosis. To further demonstrate the effectiveness of MSKACNN in bearing fault identification, a bearing vibration data acquisition system was developed, and vibration signal acquisition was performed on rolling bearings under five different fault conditions including ball mixing. The resulting datasets were used to analyze the performance of our proposed model. To validate the adaptive ability of MSKACNN, fault test data from the Case Western Reserve University Bearing Data Center were also used. Test results show that MSKACNN can identify the different bearing conditions with high accuracy with high generalization ability. We presented an implementation of the MSKACNN as a lightweight module for a real-time bearing fault diagnosis system that is suitable for production.

Rolling bearing fault diagnosis based on multi-scale weighted visibility graph and multi-channel graph convolution network

“…* Author to whom any correspondence should be addressed. neural network-based methods [4][5][6] have become an import part of data-driven fault diagnosis methods due to their abilities to extract features from raw signals without prior knowledge. However, traditional neural networks cannot exploit the connection within the data, which limits the performance of the model.…”