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Proceedings Paper

Real-time diagnosis with sensors of uncertain quality
Author(s): Ozgur Erdinc; Chaitra Raghavendra; Peter K. Willett; Thia Kirubarajan
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Paper Abstract

This paper presents a real time approach to the detection and isolation of component failures in largescale systems. The algorithm is given a set of observed test results from multiple sensors, and its main task is to deal with sensor errors (i.e., noise). The probabilities of these missed detections and false alarms are not known a-priori, and must be estimated - ideally along with the accuracies of these estimates - online, within the inference engine. Further, recognizing a practical concern in most real systems, a sparsely instantiated observation vector must not be a problem. The key ingredients to the approach include the Multiple Hypothesis Tracking (MHT) philosophy to complexity management, and a Beta prior distribution on the sensor errors. We provide results illustrating performance in terms of both computational needs and error rate, and show its application both as a filter (i.e., used to "clean" sensor reports) and as a standalone state estimator.

Paper Details

Date Published: 8 August 2003
PDF: 12 pages
Proc. SPIE 5107, System Diagnosis and Prognosis: Security and Condition Monitoring Issues III, (8 August 2003); doi: 10.1117/12.486980
Show Author Affiliations
Ozgur Erdinc, Univ. of Connecticut (United States)
Chaitra Raghavendra, Univ. of Connecticut (United States)
Peter K. Willett, Univ. of Connecticut (United States)
Thia Kirubarajan, McMaster Univ. (Canada)

Published in SPIE Proceedings Vol. 5107:
System Diagnosis and Prognosis: Security and Condition Monitoring Issues III
Peter K. Willett; Thiagalingam Kirubarajan, Editor(s)

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