Data augmentation for rotate machinery fault diagnosis with deep learning system

A challenging aspect of applying Artificial Intelligence (AI) for electric machinery fault diagnosis systems in practical engineering scenarios is to collect adequate cases with sufficient faulty conditions for training and validation. In particular, imbalanced datasets, such as motor vibration signals having much more data toward no-fault or common fault conditions than the rare ones, usually lead to biased predictions or misdiagnosis. In this paper, a statistical feature extraction model, the proposed Synthesis Data Augmentation Method and the Deep Neural Network (DNN) are investigated and implemented to enhance the classification accuracy from vibration signal measurements.