LT-Mamba: A Novel Network for RUL Estimation of Multisensor Signals

Remaining useful life (RUL) prediction is a core task in the prognostics and health management (PHM) of mechanical systems, which helps to guide preventive maintenance to improve the reliability of industrial systems. However, current models struggle to effectively model the “implicit operating condition” changes caused by the coupling of degradation in multiple components when faced with complex machinery. Therefore, in this article, we propose a multichannel integrated network based on state-space models, called the LT-Mamba network, which models operating conditions while providing deep interpretability. Specifically, we introduce a Mamba model based on state-space models (SSMs) to enhance the model’s perception and fitting capabilities for “implicit operating condition” variations. Additionally, we augment Mamba’s global observation capabilities with a trend auxiliary attention mechanism (TAAM) and LSTM to increase sensitivity to changes in degradation rates. Finally, we conduct life prediction by integrating features extracted from various models. We conducted benchmark experiments using the C-MAPSS dataset and the Milling dataset. The results demonstrate that the proposed method outperforms the existing methods.