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 correlations. This block is coupled with a temporal adaptive message passing block through an \textit{mixing} operator, yielding a multi-faceted representation of ECP logs accounting for both step-wise and variate-wise correlations. Concurrently, to tackle the second issue, we employ a sparse message passing regularizer to filter out spurious correlations. We validate the efficacy of AttentionMixer using two real-world datasets from the radiation monitoring network for Chinese nuclear power plants.

翻译：可信过程监控旨在构建一个精确且可解释的监控框架，这对于确保在高温高压等极端工况下运行的能源转换电厂的安全性至关重要。然而，当代自注意力模型在该领域中存在两个主要不足。首先，它们依赖逐步相关性，未能包含能源转换电厂日志中具有物理意义的语义信息，导致精度和可解释性欠佳。其次，注意力矩阵常充斥着虚假相关性，掩盖了物理意义的相关性，进一步阻碍了有效解释。为解决这些问题，我们提出AttentionMixer，一个旨在提升现有方法精度和可解释性的框架，并建立可信的能源转换电厂监控框架。具体而言，针对第一个问题，我们采用空间自适应消息传递模块来捕获变量间相关性。该模块通过一个混合算子与时间自适应消息传递模块耦合，生成能源转换电厂日志的多方面表征，兼顾逐步相关性和变量间相关性。同时，针对第二个问题，我们采用稀疏消息传递正则化器来过滤虚假相关性。我们使用来自中国核电站辐射监测网络的两个真实数据集验证了AttentionMixer的有效性。