A $T^{2}$-Tensor-Aided Multiscale Transformer for Remaining Useful Life Prediction in IIoT

Abstract

Industrial Internet of Things data incorporate the fundamental elements of industrial processes, providing novel paradigms of predictive maintenance for complex industrial equipment. Remaining useful life prediction is critical in the predictive maintenance task of product lifecycle management, which has attracted increasing research attention. However, most existing prediction methods cannot effectively extract complex multiscale temporal patterns and cannot meet the real-time requirements of industrial sites. To address these issues, we propose a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$T^{2}$</tex-math></inline-formula> -Tensor-aided multiscale transformer for accurate and effective prediction in this article. We defined the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$T^{2}$</tex-math></inline-formula> -tensor to represent the multiscale temporal pattern by reconstructing the time series. Besides, a high-order transformer for multiscale feature extraction is proposed. Particularly, the multiscale characteristics can be captured through intertoken and intratoken. In addition, a transformer parameter lightweighting method with tensor ring decomposition is developed. Experiments demonstrate the accuracy and efficiency of the proposed method.