Online fault diagnosis method based on Incremental Support Vector Data Description and Extreme Learning Machine with incremental output structure
| Autor: | Hongbo Fan, Gang Yin, Zhining Li, Yingtang Zhang, Guo-Quan Ren |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
business.industry
Computer science Cognitive Neuroscience Condition monitoring Hardware_PERFORMANCEANDRELIABILITY computer.software_genre Machine learning Fault (power engineering) Computer Science Applications Support vector machine Stuck-at fault Artificial Intelligence Fault coverage Artificial intelligence Data mining Fault model business Failure mode and effects analysis computer Extreme learning machine |
| Zdroj: | Neurocomputing. 128:224-231 |
| ISSN: | 0925-2312 |
| DOI: | 10.1016/j.neucom.2013.01.061 |
| Popis: | Online fault diagnosis system should be able to detect faults, recognize fault types and update the discriminating ability and knowledge of itself automatically in real time. But the class number in fault diagnosis is not constant and it is in a dynamic state with new members enrolled. The traditional recognition algorithms are not able to update diagnosis system efficiently when the class number of failure modes is increasing. To solve the problem, an online fault diagnosis method based on Incremental Support Vector Data Description (ISVDD) and Extreme Learning Machine with incremental output structure (IOELM) is proposed. ISVDD is used to find a new failure mode quickly in the continuous condition monitoring of the equipments. The fixed structure of Extreme Learning Machine is changed into an elastic structure whose output nodes could be added incrementally to recognize the new fault mode efficiently. Recognition experiments on the diesel engine under eleven different conditions show that the online fault diagnosis method based on ISVDD and IOELM works well, and the method is also feasible in fault diagnosis of other mechanical equipments. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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