Limited Fault Data Augmentation With Compressed Sensing for Bearing Fault Diagnosis

Abstract

Sufficient data is necessary for intelligent fault diagnostic approaches. However, in practice, it is often the case that only limited fault data is available due to various reasons, making it a challenge to accurately identify the health condition of bearings. To deal with the limited fault data issue, data augmentation strategies, such as generative adversarial network (GAN), are widely utilized. However, GAN has the disadvantages of being difficult to train and restricted ability to generate new data when the fault sample size is limited. Specifically, GAN requires a long training time and abundant training data to make the distribution of generated data closer to the distribution of actual data. This article presents a novel data augmentation approach with compressed sensing for fault diagnosis of bearings to better address the issue of limited fault data. The generated data by compressed sensing is diverse. In addition, the generated data is highly similar to the original data in frequency domain, thus retaining the main feature information of the original data. Furthermore, data augmentation achieved through compressed sensing requires less training data and has lower computational complexity. For bearing fault diagnosis under limited failure data, the limited fault data is first augmented based on compressed sensing, allowing for high fidelity reconstruction and high diversity data generation. Then, the augmented data is utilized to train a deep convolutional neural network to automatically learn and extract features for fault identification. The effectiveness of the presented approach is verified using two bearing datasets.