Reliable fault diagnosis using evidential aggregated residual network under varying working conditions and noise interference

Abstract

Deep neural networks (DNNs) have been widely used for intelligent fault diagnosis under the implicit assumption that the testing data draws from the same distribution of the training data. However, the assumption often falls short in open-world scenarios due to the varying working conditions and noise interference, resulting in unreliable fault diagnosis behavior. To address these issues, this paper develops an evidential aggregated residual network (EAResNet) incorporated by evidence theory, quantifying the predictive uncertainty that can be a sign of trustworthiness. By placing a Dirichlet distribution over class probabilities, accurate prediction and uncertainty estimation can be achieved. More evidence would be assigned to the correct class assisted by adopting the specific evidential loss function. The proposed aggregated residual network with the multi-branch feature learning architecture has been employed as the deep classifier in varying working conditions. In addition, a joint denoising method that merges fast iterative filtering (FIF) and independent component analysis (ICA), with fuzzy entropy threshold discriminant to filter noise components, has been developed to reduce data uncertainty caused by noise interference. Experimental results demonstrate that the proposed framework successfully achieves accurate and trustworthy prediction in varying working conditions and noise interference. Moreover, uncertainty estimation offers a confidence boundary for the predictions, mitigating the risk of erroneous decision-making.