Classification of Rolling Bearing Fault Based on Long Short Term Memory Neural Network

Abstract

Bearings are the most prevalent and easily damaged mechanical components in mechanical systems. Nowadays, Deep learning emerges as a very efficient artificial intelligence method that offers a new approach to feature extraction from raw data. Long short-term memory (LSTM) neural network is a promising type of deep learning, which is mainly used for the diagnosis of faults. In this work, a new intelligent fault diagnosis and classification method based on Long Short-Term Memory (LSTM) neural network is developed. The developed model correctly classifies the different types of faults at different running conditions in real operation conditions, and the results are compared with existing techniques. The performance of the model is evaluated with various performance metrics such as Precision, Recall, F1 score, Receiver Operating Characteristics, Area under Curve, and Accuracy. The best results are obtained from the developed LSTM model than from the existing work.