Publication Type:

Journal Article

Source:

Mechanical Systems and Signal Processing, Volume 24, Number 6, p.1887 - 1906 (2010)

URL:

http://www.sciencedirect.com/science/article/pii/S0888327010000415

Keywords:

Principle component analysis

Abstract:

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

Notes:

cited By (since 1996)15

Cite this Research Publication

N. R. Sakthivel, Sugumaran, V., and B.B. Nair, “Comparison of decision tree-fuzzy and rough set-fuzzy methods for fault categorization of mono-block centrifugal pump”, Mechanical Systems and Signal Processing, vol. 24, pp. 1887 - 1906, 2010.