Bearing Fault Diagnosis Using a Vector-Based Convolutional Fuzzy Neural Network

Abstract

The spindle of a machine tool plays a key role in machining because the wear of a spindle might result in inaccurate production and decreased productivity. To understand the condition of a machine tool, a vector-based convolutional fuzzy neural network (vector-CFNN) was developed in this study to diagnose faults from signals. The developed vector-CFNN mainly comprises a feature extraction part and a classification part. The feature extraction phase encompasses the use of convolutional layers and pooling layers, while the classification phase is facilitated through the deployment of a fuzzy neural network. The fusion layer plays an important role by being placed between the feature extraction and classification parts. It combines the characteristics and then passes the feature information to the classification part to improve the model’s performance. The developed vector-CFNN was experimentally evaluated against existing fusion methods; vector-CFNN required fewer parameters and achieved the highest average accuracy (99.84%) in fault diagnosis relative to conventional neural networks, fuzzy neural networks, and convolutional neural networks. Moreover, vector-CFNN achieved superior fault diagnosis using spindle vibration signals and required fewer parameters relative to its counterparts, indicating its feasibility for online spindle vibration monitoring.

Keywords

• spindle vibration
• vector convolutional neural network
• feature fusion
• fault diagnosis