An artificial neural network based on adaptive resonance theory for fault classification on an automated assembly machine

Simple item page
dc.contributorInternational FAIM Conference 24th : 2014 : San Antonio, Texas
dc.contributorUniversity of Texas at San Antonio. Center for Advanced Manufacturing and Lean Systems
dc.contributor.authorFernando, Heshan
dc.contributor.authorSurgenor, Brian
dc.date.accessioned2022-07-11T17:35:30Z
dc.date.available2022-07-11T17:35:30Z
dc.date.issued2014
dc.descriptionPaper presented at the Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing, held May 20-23, 2014 in San Antonio, Texas, and organized by the Center for Advanced Manufacturing and Lean Systems, University of Texas at San Antonio
dc.descriptionIncludes bibliographical references
dc.description.abstractAn unsupervised artificial neural network (ANN) based on the ART2-A algorithm is compared to a rule-based method for fault classification on an automated assembly machine. Machine data is collected using three greyscale sensors and two redundant limit switches for 11 different operating conditions. Descriptive features are extracted from the raw data and two data sets, each containing 180 feature vectors, are created for testing both methods. The first data set contains 'real' feature vectors obtained from the original sensor signals, and the second data set contains 'simulated' feature vectors obtained by scaling the 'real' feature vectors. The second data set is used to test the performance of each system when variations are present in the input space. During testing, the rule-based system correctly classified 98.3% of all feature vectors, but its classification thresholds needed to be manually adjusted to accommodate the 'simulated' data set. The ART2-A network perfectly classified the 'real' data set into 13 clusters, and then correctly classified the 'simulated' data into the same 13 clusters without any modification to the algorithm's tuning parameter, vigilance.
dc.identifier.otherhttp://dx.doi.org/10.14809/faim.2014.0411
dc.identifier.urihttps://hdl.handle.net/20.500.12588/979
dc.language.isoen_US
dc.publisherDEStech Publications, Inc.
dc.relation.ispartofseriesProceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing
dc.subjectMachinery--Monitoring--Simulation methods
dc.subjectFault location (Engineering)
dc.subjectFault tolerance (Engineering)
dc.subjectNeural networks (Computer science)
dc.titleAn artificial neural network based on adaptive resonance theory for fault classification on an automated assembly machine
dc.typeArticle

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
0411.pdf
Size:
2.32 MB
Format:
Adobe Portable Document Format
Download

Collections

24th International Conference on Flexible Automation and Intelligent Manufacturing