An ensemble model with convolutional neural network by DS evidence fusion for bearing fault diagnosis

Bearing fault diagnosis is crucial for ensuring the safety and reliability of rotating machinery. In recent years, artificial intelligence technology based on machine learning has made substantial progress in the field of bearing fault diagnosis. Most existing models for bearing fault diagnosis are built using big data and deep learning algorithms and can achieve high diagnostic accuracy with sufficient fault data. However, there still exist two open issues, 1) in practical engineering, acquiring fault sample data is challenging, and it is difficult to obtain a sufficient number of samples to train the hyperparameters of deep learning models. 2) Fault diagnosis models based on individual classifiers rely heavily on prior knowledge for signal feature extraction and the selection of network structures and parameters, making it difficult to guarantee the model’s effectiveness. This paper proposes an integrated diagnostic model called DS-ELM that employs multiple extreme learning machine modules with different parameters as subclassifiers. The outputs of these modules are then fused via DS evidence fusion theory to obtain the final diagnostic result. This ensemble model has better flexibility and robustness which significantly improves the accuracy and stability of the diagnostic model. Overall, the proposed DS-ELM provides a new solution for bearing fault diagnosis. In addition, the superiority of the reported technique is confirmed via experimental bearing fault data from Case Western Reserve University.