Abstract In order to conduct efficient process monitoring of modern industrial system featured with complexity, distributed and high-dimensional, a distributed dictionary learning is proposed for fault detection and fault isolation task. Firstly, it can reduce the computational complexity by decomposing the whole high-dimensional industrial system into several low-dimensional modules, and some prior process knowledge is integrated into the data-driven model to ensure the reliability during the decomposition stage. Secondly, since the small failure is easy to hide in high-dimensional data, it is more conducive to detecting the process data by using the sub-modules. Based on this, a Bayesian inference method is presented to fuse the distributed results for global industrial process monitoring. For the fault samples which have been detected successfully, a count time based method is introduced to determine the fault location on the block level. Then, a sparse contribution plot method is used to locate the failure source of the system on the variable level further. In the end, the performance of the proposed method is verified on a numerical simulation case, the Tennessee Eastman (TE) benchmark and an aluminum electrolysis process.

中文翻译：

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用于高维过程监控的分布式字典学习

摘要 为了对复杂、分布式、高维的现代工业系统进行高效的过程监控，提出了一种分布式字典学习用于故障检测和故障隔离任务。首先，它可以通过将整个高维工业系统分解成若干个低维模块来降低计算复杂度，并将一些先验过程知识集成到数据驱动模型中，以保证分解阶段的可靠性。其次，由于小故障容易隐藏在高维数据中，更利于使用子模块检测过程数据。在此基础上，提出了一种贝叶斯推理方法来融合分布式结果，用于全球工业过程监控。对于已成功检测到的故障样本，引入基于计数时间的方法来确定块级别的故障位置。然后，采用稀疏贡献图方法在变量层面进一步定位系统的故障源。最后，在数值模拟案例、田纳西伊士曼 (TE) 基准和铝电解过程中验证了所提出方法的性能。