An uncertainty perception metric network for machinery fault diagnosis under limited noisy source domain and scarce noisy unknown domain

Deep learning has made notable advances in intelligent fault diagnosis. However, industrial application of deep learning models faces challenges due to noise interference and scarce labeled samples. Targeting the above problems, this paper proposes a metric network-based diagnostic method. For the problem of noise, a multi-scale cross feature extraction module (MSCM) is constructed to mine key classification information under noise interference to improve fault identifiability. Different from the current approach to metric learning, this paper models the uncertainty of the similarity between ‘query sample-class prototype’, and develops corresponding loss function for more effective perception, thereby better improving the fault recognition ability of the model under limited noisy source domain and scarce noisy unknown domain. Meanwhile, to visualize the decision-making process of the model under uncertainty and improve interpretability, this paper develops a novel colony-based class activation mapping (Colony-CAM) tool, which is more reliable and focused. The proposed method is compared with five baselines across three datasets. It achieved leading diagnostic accuracies of 98.55% and 94.33% with 70 and 40 noisy training samples, respectively.