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Health monitoring of industrial processes — Challenges and solutions

机译:工业过程的健康监控—挑战与解决方案

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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.
机译:为了确保日益复杂的工业过程的健康和可靠性,必须对健康监控系统进行研究和应用。考虑到数学建模的难度和大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更具优势。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的,而且意义重大。在这次演讲中,分别研究了三种情况,即静止工况,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修改,基于关键成分识别的高级方案以及新颖的数据驱动的自适应方案,它们均提供了增强的健康监控性能。

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