Show simple item record

dc.contributor.authorSiddique, A
dc.contributor.authorYadava, G S
dc.contributor.authorSingh, B
dc.date.accessioned2006-08-11T03:50:26Z
dc.date.accessioned2019-02-10T13:58:36Z
dc.date.accessioned2019-02-11T06:29:45Z
dc.date.available2006-08-11T03:50:26Z
dc.date.available2019-02-10T13:58:36Z
dc.date.available2019-02-11T06:29:45Z
dc.date.issued2003
dc.identifier.citationDiagnostics for Electric Machines, Power Electronics and Drives, SDEMPED 4th IEEE International Symposium on, 29 - 34p.en
dc.identifier.urihttp://localhost:8080/iit/handle/2074/2099
dc.description.abstractThe on-line fault diagnostics technology for induction machines is fast emerging for the detection of incipient faults as to avoid the unexpected failure. Approximately 30-40 % faults of induction machines are stator faults. This paper presents a review of developments in applications of artificial intelligence techniques for induction machine stator fault diagnostics. Now a days artificial intelligence (AI) techniques are being preferred over traditional protective relays for fault diagnostics of induction machines. The application of expert system, fuzzy logic system, artificial neural networks, genetic algorithm have been considered for fault diagnostics. These systems and techniques can be integrated into each other with more traditional techniques. A brief description of various AI techniques highlighting the merits and demerits of each other have been discussed. Fault diagnosis of electric motor drive systems using AI techniques has been considered. The futuristic trends are also indicated.en
dc.format.extent84133 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.subjectstator fault diagnosticsen
dc.subjectartificial intelligenceen
dc.subjectartificial neural networksen
dc.titleApplications of artificial intelligence techniques for induction machine stator fault diagnostics: reviewen
dc.typeArticleen


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record