NDLI: New results on discrete-event counting under reliable and unreliable observation information

Content Provider	IEEE Xplore Digital Library
Author	Tae-Sic Yoo Garcia, H.E.
Copyright Year	2005
Description	Author affiliation: Argonne Nat. Lab., IL, USA (Tae-Sic Yoo; Garcia, H.E.)
Abstract	We present an approach for addressing the issues of detecting repeated fault events in the framework of model-based monitoring of discrete-event systems (DES) under reliable and unreliable observation information. The analysis task is to determine whether a certain observation configuration is capable of reporting the occurrence of fault events while satisfying the performance requirements. If the reliability of observation information is assured, the assessment is accomplished by evaluating diagnosability notions of interest. To evaluate the notions of diagnosability regarding repeated fault counting, polynomial-time verification algorithms are developed. In order to deal with unreliable observation information, the concept of detection confidence is introduced, which measures the quality of fault counting. An algorithm computing detection confidence is conjectured. For online fault counting, we develop a new online fault counting algorithm assuming observation reliability. The developed algorithm has lower time and space complexities than an online diagnosis algorithm reported in literature for counting the occurrence of repeated faults. This online algorithm is naturally extended to handle the unreliable observation information.
Starting Page	688
Ending Page	693
File Size	1242419
Page Count	6
File Format	PDF
ISBN	0780388127
DOI	10.1109/ICNSC.2005.1461273
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-03-19
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Delay Monitoring Fault detection Laboratories Fault diagnosis Sensor systems and applications Safety Event detection Performance analysis Polynomials
Content Type	Text
Resource Type	Article

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