ABSTRACT
Recent researches on intelligent fault diagnosis algorithms can achieve great progress; however, considering the practical scenarios, the amount of labeled data is insufficient in face of the difficulty of data annotation, which would raise the risk of overfitting and hinder the model from its industrial applications. To address this problem, this chapter proposes an inter-instance and intra-temporal self-supervised learning (ITSSL) framework, where self-supervised learning on massive unlabeled data is integrated with supervised learning on few labeled data to enrich the capacity of learnable data. Specifically, a time-amplitude signal augmentation technique is designed and inter-instance transform-consistency learning is conducted to obtain domain-invariant features. Meanwhile, an intra-temporal relation matching task is promoted to improve the temporal discriminability of the model. Moreover, to overcome the single task domination problem in this multi-task framework, an uncertainty-based dynamic weighting mechanism is utilized to automatically distribute weight for each task according to its uncertainty, which ensures the stability of multi-task optimization. Experiments on open-source and self-designed datasets demonstrate the superiority of the proposed framework over other supervised and semi-supervised methods.
