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A Novel Data Augmentation Method for Intelligent Fault Diagnosis Under Speed Fluctuation Condition

The problem of insufficient datasets has long been a hot topic in the field of prognosis and health management of rotary machines. Generative adversarial network (GAN) and other data augmentation algorithms can solve the problem of insufficient samples. However, the premise of the above method is the signal collected at a constant speed rather than at large speed fluctuation. To deal with data augmentation under large speed fluctuation, this article proposes an effective deep learning method, namely, domain adaptive efficient sub-pixel network (DAESPN). The core idea of DAESPN is to enhance the resolution of the original sample for data augmentation. The DAESPN framework is implemented as follows: after the data passes through the fully connected neural network, the multi-feature maps of the four channels are outputted. A group of high resolution (HR) features is obtained through the sub-pixel fully connected layer. In addition, maximum mean discrepancy (MMD) and mean square error (MSE) are used to construct the loss function of the model. Experimental results of gearbox and bearing datasets show that the DAESPN model has strong feasibility to carry out data augmentation for fault diagnosis of rotating machines under speed fluctuation condition. In addition, the feature learning process of DAESPN is visually displayed and analyzed.