Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation

Intelligent machinery fault diagnosis system has been receiving increasing attention recently due to the potential large benefits of maintenance cost reduction, enhanced operation safety and reliability. This paper proposes a novel deep learning method for rotating machinery fault diagnosis. Since accurately labeled data are usually difficult to obtain in real industries, data augmentation techniques are proposed to artificially create additional valid samples for model training, and the proposed method manages to achieve high diagnosis accuracy with small original training dataset. Two augmentation methods are investigated including sample-based and dataset-based methods, and five augmentation techniques are considered in general, i.e. additional Gaussian noise, masking noise, signal translation, amplitude shifting and time stretching. The effectiveness of the proposed method is validated by carrying out experiments on two popular rolling bearing datasets. Fairly high diagnosis accuracy up to 99.9% can be obtained using limited training data. By comparing with the latest advanced researches on the same datasets, the superiority of the proposed method is demonstrated. Furthermore, the diagnostic performance of the deep neural network is extensively evaluated with respect to data augmentation strength, network depth and so forth. The results of this study suggest that the proposed intelligent fault diagnosis method offers a new and promising approach.