Intelligent fault diagnosis of rotating machinery based on one-dimensional convolutional neural network

Abstract Fault diagnosis of rotating machinery plays a significant role in the reliability and safety of modern industrial systems. The traditional fault diagnosis methods usually need manually extracting the features from raw sensor data before classifying them with pattern recognition models. This requires much professional knowledge and complex feature extraction, only to cause results in a poor flexibility of the model, which only applies to the diagnosis of a fault in particular equipment. In recent years, deep learning has developed rapidly, and great achievements have been made in image analysis, speech recognition and natural language processing. However, its application in fault diagnosis of rotating machinery is still at the initial stage. In order to solve the problem of end-to-end fault diagnosis, this paper focuses on developing a convolutional neural network to learn features directly from the original vibration signals and then diagnose faults. The effectiveness of the proposed method is validated through PHM (Prognostics and Health Management) 2009 gearbox challenge data and a planetary gearbox test rig. Compared with the other three traditional methods, the results show that the one-dimensional convolutional neural network (1-DCNN) model has higher accuracy for fixed-shaft gearbox and planetary gearbox fault diagnosis than that of the traditional diagnostic ones.