A Novel Fault Diagnosis Method based on Convolutional Neural Network with Adaptive Noise Injection

Abstract In most existing intelligent fault diagnosis methods, noise is considered harmful and may decrease diagnosis accuracy. In contrast to these methods, this study proposes a novel fault diagnosis method with extra noise injection, termed an adaptive-noise-injected convolutional neural network (ANI-CNN). Noise is intentionally injected into a CNN model's softmax layer to improve fault diagnosis accuracy. The injected noise is used in the iteration of the CNN model and adaptively adjusted according to the change in model loss. Bearing datasets from Case Western Reserve University and Paderborn University were used to validate the effectiveness of the proposed method. The robustness of the proposed method was illustrated by injecting Gaussian and uniform noise. By comparing the ablation study results with those of the state-of-the-art methods, and t-test results before and after noise injection, the effectiveness of noise injection in enhancing diagnosis accuracy was demonstrated. The proposed method performed well on small samples and in complex working conditions, and possesses good generalizability and the ability to deal with real-world datasets.