[2302.10426] An Accurate and Interpretable Framework for Trustworthy Process Monitoring

Computer Science > Artificial Intelligence arXiv:2302.10426 (cs) [Submitted on 21 Feb 2023 (v1), last revised 24 Mar 2026 (this version, v3)] Title:An Accurate and Interpretable Framework for Trustworthy Process Monitoring Authors:Hao Wang, Zhiyu Wang, Yunlong Niu, Zhaoran Liu, Haozhe Li, Yilin Liao, Yuxin Huang, Xinggao Liu View a PDF of the paper titled An Accurate and Interpretable Framework for Trustworthy Process Monitoring, by Hao Wang and 7 other authors View PDF HTML (experimental) Abstract:Trustworthy process monitoring seeks to build an accurate and interpretable monitoring framework, which is critical for ensuring the safety of energy conversion plant (ECP) that operates under extreme working conditions such as high pressure and temperature. Contemporary self-attentive models, however, fall short in this domain for two main reasons. First, they rely on step-wise correlations that fail to involve physically meaningful semantics in ECP logs, resulting in suboptimal accuracy and interpretability. Second, attention matrices are frequently cluttered with spurious correlations that obscure physically meaningful ones, further impeding effective interpretation. To overcome these issues, we propose AttentionMixer, a framework aimed at improving both accuracy and interpretability of existing methods and establish a trustworthy ECP monitoring framework. Specifically, to tackle the first issue, we employ a spatial adaptive message passing block to capture variate-wise corre...