SIMTSeg: A self-supervised multivariate time series segmentation method with periodic subspace projection and reverse diffusion for industrial process

Abstract

Subsequences with varied regimes in the industrial multivariate time series (MTS) are closely associated with the dynamic status of the multi-phased industrial process. Time series segmentation (TSS) provides insights into the underlying behavior of industrial systems. However, the complexity of industrial data poses significant challenges to the conventional TSS methods. Motivated by this, a Self-supervised Industrial Multivariate Time-series Segmentation method (SIMTSeg) is presented in this work. An MTS folding module based on Ramanujan periodic subspace projection is first proposed, where the MTS is reshaped into the 3D feature map to realize the compact representation of the intricate data dependencies. Subsequently, a self-supervised module based on the encoder-decoder architecture is adopted to address the problem of deficient and task-specific annotations in industrial data. The folded feature map is denoised step by step following the reverse diffusion process, and finally turns into the segmentation mask without redundant details. The proposed SIMTSeg has been validated by a popular industrial benchmark, the Tennessee Eastman Process, and outperforms the unsupervised data-driven baselines in terms of various performance metrics. SIMTSeg has no prerequisite on the number of segmentation points or regime types, and is capable of giving more meaningful segmentation results that are in line with the high-level semantics.