工业过程的健康监测。挑战和解决方案

Shen Yin
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引用次数: 0

摘要

为了保证日益复杂的工业过程的健康和可靠性,健康监测系统的研究和应用是必要的。考虑到数学建模的难度和海量过程数据的可用性,所谓的数据驱动方法比基于模型的方法具有优势。因此,为特定的工业条件设计有效的数据驱动的健康监测方案是有希望和重要的。在这次演讲中,分别研究了三种情况,即固定操作条件,动态过程和涉及变化的大规模过程。相应地,提出了对标准多元统计方法的改进、基于关键成分识别的改进方案和一种新的数据驱动自适应方案,提高了健康监测的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Health monitoring of industrial processes — Challenges and solutions
To ensure the health and reliability of increasingly complicated industrial processes, the study and application of health monitoring systems is necessary. Considering the difficulty of mathematical modeling and the availability of massive process data, the so-called data-driven methods gain advantage over model-based ones. Therefore, it is promising and significant to design efficient data-driven health monitoring schemes for particular industrial conditions. In this talk, three circumstances, i.e. stationary operating conditions, dynamic processes and large-scale processes involving changes, are investigated respectively. Correspondingly, the modifications of the standard multivariate statistical approaches, the advanced schemes based on the identification of key components and a novel data-driven adaptive scheme are proposed, which all provide enhanced health monitoring performance.
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