An ARTMAP neural network-based machine condition monitoring system

Presents a real-time neural network-based condition monitoring system for rotating mechanical equipment. At its core is an ARTMAP neural network, which continually monitors machine vibration data, as it becomes available, in an effort to pinpoint new information about the machine condition. As new f...

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Bibliographic Details
Published inJournal of quality in maintenance engineering Vol. 6; no. 2; pp. 86 - 105
Main Authors Knapp, Gerald M, Javadpour, Roya, Wang, Hsu-Pin (Ben)
Format Journal Article
LanguageEnglish
Published Bradford MCB UP Ltd 01.06.2000
Emerald Group Publishing Limited
Subjects
Accuracy
Back propagation
Diagnostics
Failure
Fault diagnosis
Feedback
Hypotheses
Machinery
Maintenance management
Monitoring systems
Neural networks
Noise
Reliability engineering
Studies
Theory
Condition monitoring
Neural networks
Maintenance
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Summary:Presents a real-time neural network-based condition monitoring system for rotating mechanical equipment. At its core is an ARTMAP neural network, which continually monitors machine vibration data, as it becomes available, in an effort to pinpoint new information about the machine condition. As new faults are encountered, the network weights can be automatically and incrementally adapted to incorporate information necessary to identify the fault in the future. Describes the design, operation, and performance of the diagnostic system. The system was able to identify the presence of fault conditions with 100 percent accuracy on both lab and industrial data after minimal training; the accuracy of the fault classification (when trained to recognize multiple faults) was greater than 90 percent.
Bibliography:ObjectType-Article-2
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ISSN:1355-2511
1758-7832
DOI:10.1108/13552510010328095