A novel method for bearing fault diagnosis based on BiLSTM neural networks

In recent years, research work on intelligent data-driven bearing fault diagnosis methods has received increasing attention. The detection of a fault, whether incipient or moderate, and the monitoring of its evolution are a major challenge in the field of fault diagnosis and are of great industrial interest. For an efficient identification of this type of fault, we propose in this paper a new method of bearing fault diagnosis (“novel BiLSTM” method). This new approach contributes to the improvement of fault diagnosis methods based on BiLSTM networks. The performance was tested under sixteen conditions and for different loads using the Case Western Reserve University (CWRU) bearing dataset under conditions higher than those proposed in the literature dealing with the same problem. The experimental results obtained show that the proposed method has excellent performance. Subsequently, the proposed method was experimentally compared with the CNN model. The results of this comparison showed that the model developed in this paper not only has a higher accuracy rate in the test set but also in the learning process.