An end-to-end fault diagnostics method based on convolutional neural network for rotating machinery with multiple case studies

The fault diagnostics of rotating components are crucial for most mechanical systems since the rotating components faults are the main form of failures of many mechanical systems. In traditional diagnostics approaches, extracting features from raw input is an important prerequisite and normally requires manual extraction based on signal processing techniques. This suffers of some drawbacks such as the strong dependence on domain expertise, the high sensitivity to different mechanical systems, the poor flexibility and generalization ability, and the limitations of mining new features, etc. In this paper, we proposed an end-to-end fault diagnostics model based on a convolutional neural network for rotating machinery using vibration signals. The model learns features directly from the one-dimensional raw vibration signals without any manual feature extraction. To fully validate its effectiveness and robustness, the proposed model is tested on four datasets, including two public ones and two datasets of our own, covering the applications of ball screw, bearing and gearbox. The method of manual, signal processing based feature extraction combined with a classifier is also explored for comparison. The results show that the manually extracted features are sensitive to the various applications, thus needing fine-tuning, while the proposed framework has a good robustness for rotating machinery fault diagnostics with high accuracies for all the four applications, without any application-specific manual fine-tuning.