Fault Detection, Isolation, and Distinguishing Between Sensor Failures and Disturbances

Autor:	Fuat Doymaz, Ahmet Palazoglu, Jose A. Romagnoli
Rok vydání:	2000
Předmět:	Engineering Identification (information) business.industry Calibration (statistics) Principal component analysis Real-time computing Benchmark (computing) Process (computing) Isolation (database systems) Root cause business Fault detection and isolation
Zdroj:	IFAC Proceedings Volumes. 33:39-44
ISSN:	1474-6670
DOI:	10.1016/s1474-6670(17)38515-4
Popis:	A novel approach is proposed to isolate the sensors that are affected by the root cause of non-conforming operation and to distinguish between the failed sensors and process upsets. Systems having multivariate nature can be monitored by building a principal component analysis model using historical data. T2 and sum of squared prediction error (SPE) of the calibration model facilitate fault detection and isolation online. These two measures are complimentary in explaining the events captured and not captured by the model. In this paper, we put more emphasis on the importance of using the T2 and the SPE together for fault detection, identification, and distinguishing between sensor failures and disturbances. This is illustrated on a benchmark industrial liquid-fed ceramic melter.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b934ddbda6bec772d9ad28bd6ce34201 https://doi.org/10.1016/s1474-6670(17)38515-4 Zobrazit plný text záznamu