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Rough-Sets-Based Reduction for Analog Systems Diagnostics
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AI
DOI：10.1109/TIM.2010.2060225.png)
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Abstract
En 中文
This paper presents an application of the rough sets algorithms to the analog systems diagnostics. Multiple methods of discretization and reduction of the data sets obtained from measurements are tested to find effective combinations for diagnostic purposes. Useful features of rough sets are identified. Versatility and scalability of the proposed method are verified, by application to multiple analog systems belonging to various technical domains with different complexities. Practical remarks about fault detection and location in the presented systems are included. This paper is supplemented by conclusions about the effectiveness of the method and its future prospects.
Keywords:
Analog systems
diagnostics
fault detection
rough sets
soft computing
Journal
IF:
5.9
Papers:
2.0W
Citations:
5.8W
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