Zhixin Huang, Christian Gruhl and Bernhard Sick have presented a conference paper at the IEEE Conference on Industrial Electronics and Applications (ICIEA). The paper titled LiST: An All-Linear-Layer Spatial-Temporal Feature Extractor with Uncertainty Estimation for RUL Prediction is about:

In the context of Remaining Useful Life (RUL) prediction for industrial systems, the pursuit of prediction accuracy must be balanced against the hardware costs of model operation and the reliability of prediction results. To resolve these challenges, we introduce LiST, an all-linear-layer spatial-temporal feature extractor integrated with uncertainty estimation, specifically designed for processing sensor multivariate time series (MTS) data. Unlike traditional linear models that flatten MTS and thus neglect their spatial-temporal dependencies, LiST's linear layers act on both the sensor and time dimensions of MTS that can extract spatial and temporal features like GNN and RNN models. Through performance comparisons on four RUL prediction datasets, LiST uses only 66.1% of the parameters, achieves comparable accuracy to state-of-the-art GNN and RNN models, obtains the best results on two datasets with up to a 21.6% improvement in Score, and enhances training efficiency by 3.2 times. Additionally, LiST can predict RUL with uncertainty estimation and precisely disentangle epistemic and aleatoric uncertainties, thus enhancing the model's practicality and reliability in real-world industrial applications.

Zhixin Huang, Christian Gruhl and Bernhard Sick have presented a conference paper at the IEEE Conference on Industrial Electronics and Applications (ICIEA). The paper titled LiST: An All-Linear-Layer Spatial-Temporal Feature Extractor with Uncertainty Estimation for RUL Prediction is about:

In the context of Remaining Useful Life (RUL) prediction for industrial systems, the pursuit of prediction accuracy must be balanced against the hardware costs of model operation and the reliability of prediction results. To resolve these challenges, we introduce LiST, an all-linear-layer spatial-temporal feature extractor integrated with uncertainty estimation, specifically designed for processing sensor multivariate time series (MTS) data. Unlike traditional linear models that flatten MTS and thus neglect their spatial-temporal dependencies, LiST's linear layers act on both the sensor and time dimensions of MTS that can extract spatial and temporal features like GNN and RNN models. Through performance comparisons on four RUL prediction datasets, LiST uses only 66.1% of the parameters, achieves comparable accuracy to state-of-the-art GNN and RNN models, obtains the best results on two datasets with up to a 21.6% improvement in Score, and enhances training efficiency by 3.2 times. Additionally, LiST can predict RUL with uncertainty estimation and precisely disentangle epistemic and aleatoric uncertainties, thus enhancing the model's practicality and reliability in real-world industrial applications.