Original scientific paper

https://doi.org/10.17559/TV-20220512135354

Bearing Fault Diagnosis Based on Wide Deep Convolutional Neural Network and Long Short Term Memory

Zijian Chen ; University of Science and Technology Liaoning, School of Computer Science and Software Engineering, No. 189, Qianshan Road, Anhsan, Liaoning, China
Ji Zhao ; University of Science and Technology Liaoning, School of Computer Science and Software Engineering, No. 189, Qianshan Road, Anhsan, Liaoning, China

Abstract

Mechanical fault can cause economic loss of different degrees, even casualties. Timely fault diagnosis is an essential condition for ensuring safe production in modern industry. With the growth of intelligent manufacturing, more and more attention is paid to fault diagnosis methods that are based on deep learning. However, the diagnostic accuracy of existing diagnostic methods has still to be improved. Therefore, a fault diagnosis method called WDCNN-LSTM is proposed by combining Wide First-layer Deep Convolutional Neural Network with Long and Short Term Memory. Feature information is extracted adaptively from one-dimensional original vibration signals by Convolutional Neural Network. The extracted features are further extracted by Long and Short Term Memory, so that the fault feature information can be fully obtained. Experiments are performed on CWRU datasets to verify our proposed method. By analyzing the experimental results, we find that the average accuracy of the proposed WDCNN-LSTM model is 99.65%.

Keywords

bearing fault diagnosis; deep learning; mechanical equipment; vibration signal

Hrčak ID:

288426

URI

https://hrcak.srce.hr/288426

Publication date:

15.12.2022.

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