Characterizing discrete event timing relationships for fault monitoring of manufacturing systems

Sujit R. Das; Lawrence E. Holloway

doi:10.1109/CCA.1996.559054

Characterizing discrete event timing relationships for fault monitoring of manufacturing systems

Sujit R. Das, Lawrence E. Holloway

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

The timing and sequencing relationships of changes (events) in discrete sensors and actuators can be used to determine whether a manufacturing system is operating as expected. In this paper, we present a method of learning inter-event timing relationships using observations from a correctly operating system. The observed sample statistics characteristic of correct system operation are used to create a confidence space of possible timing relationships (acceptable delay intervals) of the underlying system. Given a relative cost of false alarms vs. missed detections, the timing relationships can be chosen to minimize the worst case total of the false alarm and missed detection costs over the confidence space.

Original language	English
Title of host publication	Proceeding of the 1996 IEEE International Conference on Control Applications
Subtitle of host publication	held together with IEEE International Symposium on Intelligent Control
Pages	1012-1018
Number of pages	7
DOIs	https://doi.org/10.1109/CCA.1996.559054
State	Published - 1996
Event	Proceedings of the 1996 IEEE International Conference on Control Applications - Dearborn, MI, USA Duration: Sep 15 1996 → Sep 18 1996

Conference

Conference	Proceedings of the 1996 IEEE International Conference on Control Applications
City	Dearborn, MI, USA
Period	9/15/96 → 9/18/96

ASJC Scopus subject areas

Control and Systems Engineering

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10.1109/CCA.1996.559054

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@inproceedings{61df3da37b7c482581e28dac415a688b,

title = "Characterizing discrete event timing relationships for fault monitoring of manufacturing systems",

abstract = "The timing and sequencing relationships of changes (events) in discrete sensors and actuators can be used to determine whether a manufacturing system is operating as expected. In this paper, we present a method of learning inter-event timing relationships using observations from a correctly operating system. The observed sample statistics characteristic of correct system operation are used to create a confidence space of possible timing relationships (acceptable delay intervals) of the underlying system. Given a relative cost of false alarms vs. missed detections, the timing relationships can be chosen to minimize the worst case total of the false alarm and missed detection costs over the confidence space.",

author = "Das, {Sujit R.} and Holloway, {Lawrence E.}",

year = "1996",

doi = "10.1109/CCA.1996.559054",

language = "English",

pages = "1012--1018",

booktitle = "Proceeding of the 1996 IEEE International Conference on Control Applications",

note = "Proceedings of the 1996 IEEE International Conference on Control Applications ; Conference date: 15-09-1996 Through 18-09-1996",

}

Das, SR & Holloway, LE 1996, Characterizing discrete event timing relationships for fault monitoring of manufacturing systems. in Proceeding of the 1996 IEEE International Conference on Control Applications : held together with IEEE International Symposium on Intelligent Control. pp. 1012-1018, Proceedings of the 1996 IEEE International Conference on Control Applications, Dearborn, MI, USA, 9/15/96. https://doi.org/10.1109/CCA.1996.559054

Characterizing discrete event timing relationships for fault monitoring of manufacturing systems. / Das, Sujit R.; Holloway, Lawrence E.
Proceeding of the 1996 IEEE International Conference on Control Applications : held together with IEEE International Symposium on Intelligent Control. 1996. p. 1012-1018.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Holloway, Lawrence E.

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AB - The timing and sequencing relationships of changes (events) in discrete sensors and actuators can be used to determine whether a manufacturing system is operating as expected. In this paper, we present a method of learning inter-event timing relationships using observations from a correctly operating system. The observed sample statistics characteristic of correct system operation are used to create a confidence space of possible timing relationships (acceptable delay intervals) of the underlying system. Given a relative cost of false alarms vs. missed detections, the timing relationships can be chosen to minimize the worst case total of the false alarm and missed detection costs over the confidence space.

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Characterizing discrete event timing relationships for fault monitoring of manufacturing systems

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