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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Stone, G.C. Kapler, J. |
| Copyright Year | 1997 |
| Description | Author affiliation: Iris Power Eng. Inc., Etobicoke, Ont., Canada (Stone, G.C.) |
| Abstract | Many pulp and paper mills are moving toward condition-based maintenance (CBM), also known as predictive maintenance, for major equipment such as generators and motors. In research sponsored by the electric utility industry, new tools have been developed to help motor and generator operators implement CBM on the electrical windings in such machines. One technology involved the development of an expert system which analyzes all common online and offline tests together with operating data to estimate an overall indicator of the risk of winding failure. In a second development, an online partial discharge measurement system which can be used by plant personnel has been developed which can detect most of the deterioration mechanisms that can occur in stator windings rated 4 kV and above. This paper reviews the development of these two areas, which rely heavily on sophisticated software technologies, and discusses their application to implement CBM. The paper concentrates on stator windings. A short discussion of what problems are most likely to occur in stator windings is first presented. |
| Starting Page | 57 |
| Ending Page | 63 |
| File Size | 674968 |
| Page Count | 7 |
| File Format | |
| ISBN | 0780339193 |
| ISSN | 01902172 |
| DOI | 10.1109/PAPCON.1997.595216 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1997-06-16 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Stator windings Paper mills Predictive maintenance Power industry Electrical equipment industry Machine windings Expert systems Failure analysis Risk analysis System testing |
| Content Type | Text |
| Resource Type | Article |
| Subject | Industrial and Manufacturing Engineering Electrical and Electronic Engineering |
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