Real-time diagnosis with sensors of uncertain quality

Ozgur Erdinc; Chaitra Raghavendra; Peter K. Willett; Thia Kirubarajan

doi:10.1117/12.486980

8 August 2003 Real-time system diagnosis with sensors of uncertain quality

Ozgur Erdinc, Chaitra Raghavendra, Peter K. Willett, Thia Kirubarajan

Proceedings Volume 5107, System Diagnosis and Prognosis: Security and Condition Monitoring Issues III; (2003) https://doi.org/10.1117/12.486980
Event: AeroSense 2003, 2003, Orlando, Florida, United States

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.

Citation Download Citation

Ozgur Erdinc, Chaitra Raghavendra, Peter K. Willett, and Thia Kirubarajan "Real-time system diagnosis with sensors of uncertain quality", Proc. SPIE 5107, System Diagnosis and Prognosis: Security and Condition Monitoring Issues III, (8 August 2003); https://doi.org/10.1117/12.486980

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