A memory-based approach to fault detection and diagnosis
| Autor: | P. I. Ivanova, R. Kulhavy |
|---|---|
| Rok vydání: | 1999 |
| Předmět: |
Engineering
business.industry media_common.quotation_subject Fault (power engineering) Machine learning computer.software_genre Fault detection and isolation Support vector machine Supervisory control SAFER Software fault tolerance Quality (business) Artificial intelligence business Implementation computer media_common |
| Zdroj: | ECC |
| DOI: | 10.23919/ecc.1999.7100028 |
| Popis: | Fault detection and diagnosis are functions with enormous importance to advanced intelligent supervisory control systems. In the quest for improved quality and safer operations, we adopt a different approach to fault diagnosis based on the memory-based learning paradigm. The properties of memory-based methods that make them especially appropriate for autonomous systems functioning in environments that are not known in advance and in which the designers will not be able to tune the learning parameters during operation are thoroughly discussed. Some aspects of practical implementations are considered. Finally, we explore a sound approach to dealing with practical fault detection scenarios when the available database is huge. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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