AttentionMixer: An Accurate and Interpretable Framework for Process Monitoring

• URL: http://arxiv.org/abs/2302.10426v2
• Date: Wed, 10 May 2023 13:42:09 GMT
• Title: AttentionMixer: An Accurate and Interpretable Framework for Process Monitoring
• Authors: Hao Wang, Zhiyu Wang, Yunlong Niu, Zhaoran Liu, Haozhe Li, Yilin Liao, Yuxin Huang, Xinggao Liu
• Abstract summary: A data-driven approach, AttentionMixer, is proposed to establish an accurate and interpretable radiation monitoring framework for energy conversion plants. To improve the model accuracy, the first technical contribution involves the development of spatial and temporal adaptive message passing blocks. The second technical contribution involves the implementation of a sparse message passing regularizer, which eliminates spurious and noisy message passing routes.
• Score: 8.155472809416969
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: An accurate and explainable automatic monitoring system is critical for the safety of high efficiency energy conversion plants that operate under extreme working condition. Nonetheless, currently available data-driven monitoring systems often fall short in meeting the requirements for either high-accuracy or interpretability, which hinders their application in practice. To overcome this limitation, a data-driven approach, AttentionMixer, is proposed under a generalized message passing framework, with the goal of establishing an accurate and interpretable radiation monitoring framework for energy conversion plants. To improve the model accuracy, the first technical contribution involves the development of spatial and temporal adaptive message passing blocks, which enable the capture of spatial and temporal correlations, respectively; the two blocks are cascaded through a mixing operator. To enhance the model interpretability, the second technical contribution involves the implementation of a sparse message passing regularizer, which eliminates spurious and noisy message passing routes. The effectiveness of the AttentionMixer approach is validated through extensive evaluations on a monitoring benchmark collected from the national radiation monitoring network for nuclear power plants, resulting in enhanced monitoring accuracy and interpretability in practice.

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