Valve Stiction Detection Using Multitimescale Feature Consistent Constraint for Time-Series Data

Using neural networks to build a reliable fault detection model is an attractive topic in industrial processes but remains challenging due to the lack of labeled data. We propose a feature learning approach for industrial time-series data based on self-supervised contrastive learning to tackle this challenge. The proposed approach consists of two components: data transformation and representation learning. The data transformation converts the raw time-series to temporal distance matrices capable of storing temporal and spatial information. The representation learning component uses a convolution-based encoder to encode the temporal distance matrices to embedding representations. The encoder is trained using a new constraint called multitimescale feature consistent constraint. Finally, a fault detection framework for the valve stiction detection task is developed based on the feature learning method. The proposed framework is evaluated not only on an industrial benchmark dataset but also on a hardware experimental system and real industrial environments.

Automated summary

This paper proposes a feature learning approach for industrial time-series data based on self-supervised contrastive learning to address the challenge of the lack of labeled data in using neural networks to build a reliable fault detection model. The approach consists of two components: data transformation and representation learning. The data transformation converts the raw time-series into temporal distance matrices with temporal and spatial information. The representation learning component uses a convolution-based encoder to encode the temporal distance matrices into embedding representations.