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Vol.6 No.3 previousSP12 (78-79-80-81)-NT 66

Academic Articles
Regular Paper Vol.6 No.3 (2014) p.86 - p.106

Process Signal Selection Method to Improve the Impact Mitigation of Sensor Broken for Diagnosis Using Machine Learning

Hirotsugu MINOWA1, Akio GOFUKU1
1Okayama University, 3-1-1 Tsushima naka, Kita-ku, Okayama 700-8530, JAPAN
Accidents of industrial plants cause large loss on human, economic, social credibility. In recent, studies of diagnostic methods using techniques of machine learning are expected to detect early and correctly abnormality occurred in a plant. However, the general diagnostic machines are generated generally to require all process signals (hereafter, signals) for plant diagnosis. Thus if trouble occurs such as process sensor is broken, the diagnostic machine cannot diagnose or may decrease diagnostic performance.
Therefore, we propose an important process signal selection method to improve impact mitigation without reducing the diagnostic performance by reducing the adverse effect of noises on multi-agent diagnostic system. The advantage of our method is the general-purpose property that allows to be applied to various supervised machine learning and to set the various parameters to decide termination of search.
The experiment evaluation revealed that diagnostic machines generated by our method using SVM improved the impact mitigation and did not reduce performance about the diagnostic accuracy, the velocity of diagnosis, predictions of plant state near accident occurrence, in comparison with the basic diagnostic machine which diagnoses by using all signals.
This paper reports our proposed method and the results evaluated which our method was applied to the simulated abnormal of the fast-breeder reactor Monju.

Support Vector Machine(SVM), machine learning, process signals selection, plant diagnosis, Monju plant, Multi-Agent System (MAS)
Full Paper: PDF EJAM Vol.6No.3 pp.85-105 "Process signal selection method to improve the impact mitigation of sensor broken for diagnosis using machine learning"
Article Information
Article history:
Received 28 April 2014
Accepted 17 November 2014