Quadratic Support Vector Machine and K-Nearest Neighbor Based Robust Sensor Fault Detection and Isolation

Abed, Ahmed M.; Gitaffa, Sabah A.; Issa, Abbas H.

doi:10.30684/etj.v39i5A.2002

	Quadratic Support Vector Machine and K-Nearest Neighbor Based Robust Sensor Fault Detection and Isolation
Engineering and Technology Journal
Article 15, Volume 39, 5A, 2021, Pages 859-869 PDF (731.35 K)
Document Type: Research Paper
DOI: 10.30684/etj.v39i5A.2002
Authors
Ahmed M. Abed^* ¹; Sabah A. Gitaffa²; Abbas H. Issa³
¹Ministry of Oil, Oil Exploration Company - Iraq
²University of Technology, Electrical Engineering Department - Iraq
³University of Technology - Iraq
Abstract
Fault detection plays a serious role in high-cost and safety-critical processes. There are two main drivers for continuous improvement in the area of early detection of process faults safety and reliability of technical plants. Detect fault in Geophone string sensors (SG-10) are very important in oil exploration to avoid loss economy. Methods are developed to enable earlier detection of process faults than the traditional limit and trend checking based on a single process variable and the development of these methods is a key matter. Classification methods will be used for pattern recognition and as such is appropriate for fault detection. In supervised training input-output pairs, both for normal and fault conditions, are presented to the network. The models were trained on the free fault and fault sensors. Then the Quadratic Support Vector Machine (QSVM) and k-Nearest Neighbor (KNN) as the classifiers are used. The test results for measuring the performance of 1232 sample classifiers from data show that the accuracy of fault-free sensor recognition is 97.4 % and 100% consecutively for these classifiers.
Keywords
Fault Detection and Isolation(FDI); Pattern Recognition; QSVM; KNN; SG-10

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