Debiased Contrastive Learning for Time-Series Representation Learning and Fault Detection

Kexin Zhang, Rongyao Cai, Chunlin Zhou, Yong Liu

Abstract: Building reliable fault detection systems through deep neural networks is an appealing topic in industrial scenarios. In these contexts, the representations extracted by neural networks on available labeled time-series data can reflect system states. However, this endeavor remains challenging due to the necessity of labeled data. Self-supervised contrastive learning (SSCL) is one of the effective approaches to deal with this challenge, but existing SSCL-based models suffer from sampling bias and representation bias problems. This article introduces a debiased contrastive learning framework for time-series data and applies it to industrial fault detection tasks. This framework first develops the multigranularity augmented view generation method to generate augmented views at different granularities. It then introduces the momentum clustering contrastive learning strategy and the expert knowledge guidance mechanism to mitigate sampling bias and representation bias, respectively. Finally, the experiments on a public bearing fault detection dataset and a widely used valve stiction detection dataset show the effectiveness of the proposed feature learning framework.