Enhancing Bearing Fault Diagnosis With Deep Learning Model Fusion and Semantic Web Technologies

Abstract

Given the limited accuracy of a singular deep learning model in bearing fault diagnosis, this study seeks to investigate and validate the efficacy of a deep learning model fusion strategy. It also aims to enhance the performance of deep learning models in bearing fault diagnosis using semantic web technology. Utilizing a publicly available bearing dataset, we employ semantic web to represent data in a structured format that is easily interpretable by machines. We then train separate convolutional neural network (CNN) and long short-term memory network (LSTM) models, and implement three distinct fusion strategies: voting fusion, weighted fusion, and stacking fusion. The experimental results indicate that the model fusion strategy significantly improves both the accuracy and robustness of fault diagnosis. Notably, the stacking fusion strategy outperforms the others in recognizing complex fault patterns. This study offers novel ideas and methodologies for the application of deep learning in the realm of bearing fault diagnosis.