Industrial Fault Detection using Contrastive Representation Learning on Time-series Data

Abstract

Deep learning (DL) has been known as one of the effective techniques for building data-driven fault detection methods. The successful DL-based methods require the condition that massive labeled data are available, but this is sometimes an inevitable obstacle in real industrial environments. As one of the solutions, autoencoders (AEs) are widely adopted since AEs can extract features from unlabeled data. However, some challenges in AE-based fault detection methods remain, such as the design of encoder architecture, the computational cost, and the usage of the limited labeled data. This paper proposes a new industrial fault detection method through learning instance-level representation of time-series based on the self-supervised contrastive learning framework (SSCL). The proposed method uses dilated-causal-convolution-based encoder-only architecture to extract the information from industrial time-series data. A new data augmentation method for time-series data is proposed based on the temporal distance distribution, which is used to construct positive pairs in SSCL. Moreover, the encoder is alternately trained by the new weighted contrastive loss and the traditional classification loss. Finally, the experiments are conducted on the industrial data set and a semi-physical system, showing the effectiveness of the proposed method.