Outstanding Paper Award of 2020 (JCEJ) This prize is awarded annually to authors of outstanding papers published in the Journal of Chemical Engineering of Japan. The winners are carefully chosen by the editorial board.
[優秀論文賞] Physical-Principle Based Extended Attributes for Process Fault Detection
by Junqing Xia, Yoshiyuki Yamashita カ シュンケイ・山下 善之
Keywords: Fault Detection, Grey-box Process, Modeling, Process Monitoring. 故障検出、グレーボックスプロセス、モデリング、プロセスモニタリング
JOURNAL OF CHEMICAL ENGINEERING OF JAPAN https://doi.org/10.1252/jcej.20we028
プロセスの安全性、信頼性、性能、コスト効率を維持するために、プロセスモニタリングは重要である。本研究では、データ駆動型故障検出技術に、第一原理やプロセスの因果関係などのプロセス知識を組み合わせたハイブリッド故障検出手法を提案する。Process monitoring is of importance to maintain process safety, reliability, performance and cost efficiency. This work presents a hybrid fault detection approach that combines process knowledge such as first-principles and process causal relations into data-driven fault detection techniques. The process knowledge is embedded into the process dataset as the form of extended attributes (ExAs). In this paper, we discuss the benefits of adding process knowledge into the process data, as well as the procedure of extracting ExAs from available process information such as piping and instrument digraph. Our proposed method was successfully tested on the Tennessee Eastman Process using two commonly utilized data-driven fault detection techniques: principle component analysis and its variant kernel PCA.