Comparison of decision tree-fuzzy and rough set-fuzzy methods for fault categorization of mono-block centrifugal pump
作者： N.R. Sakthivel , V. Sugumaran , Binoy. B. Nair
DOI: 10.1016/J.YMSSP.2010.01.008
关键词:
摘要: Mono-block centrifugal pumps are widely used in a variety of applications. In many applications the role mono-block pump is critical and condition monitoring essential. Vibration based continuous analysis using machine learning approach gaining momentum. Particularly, artificial neural networks, fuzzy logic have been employed for fault diagnosis. This paper presents use decision tree rough sets to generate rules from statistical features extracted vibration signals under good faulty conditions pump. A classifier built set tested test data. The results obtained those compared. Finally, accuracy principle component tree-fuzzy system also evaluated. study reveals that overall classification by hybrid some extent better than set-fuzzy system.
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