Fault detection and diagnosis in manufacturing systems: A behavioral model approach

Lawrence E. Holloway, Bruce H. Krogh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

33 Scopus citations

Abstract

An approach to online fault detection and diagnosis in automated manufacturing systems with discrete controls and sensing is described. The approach is based on the concept of behavioral models of the individual system components. These models, which can be developed while the system is being designed, characterize the responses of the devices in the system to arbitrary input signals over the range of acceptable operating conditions. The expected flow of signals through the system, from control inputs to sensor outputs, is captured in the behavioral model dynamics. This model provides the basis for online fault detection by generating expected system response signals which are compared online, in real-time, to the actual sensor signals from the system. Fault diagnosis is accomplished by maintaining a current set of operational assumptions which identify the system components which could cause deviations from the expected behavior.

Original languageEnglish
Title of host publicationProc Rensselaer 2 Int Conf Comput Integr Manuf
Pages252-259
Number of pages8
StatePublished - 1990
EventProceedings of the Rensselaer's 2nd International Conference on Computer Integrated Manufacturing - Troy, NY, USA
Duration: May 21 1990May 23 1990

Publication series

NameProc Rensselaer 2 Int Conf Comput Integr Manuf

Conference

ConferenceProceedings of the Rensselaer's 2nd International Conference on Computer Integrated Manufacturing
CityTroy, NY, USA
Period5/21/905/23/90

ASJC Scopus subject areas

  • General Engineering

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