Application of standalone system and hybrid system for fault diagnosis of centrifugal pump using time domain signals and statistical features Online publication date: Sat, 23-Aug-2014
by N.R. Sakthivel; Binoy B. Nair; V. Sugumaran; Rajakumar S. Roy
International Journal of Data Mining, Modelling and Management (IJDMMM), Vol. 4, No. 1, 2012
Abstract: Centrifugal pumps are a crucial part of many industrial plants. Early detection of faults in pumps can increase their reliability, reduce energy consumption, service and maintenance costs, and increase their life-cycle and safety, thus providing a significant reduction in life-time costs. Vibration-based condition monitoring and analysis using machine learning approach is gaining momentum. Vibration monitoring can identify a number of potential pump problems such as bearing fault, impeller fault, seal fault, loose joints or fasteners, and cavitation issues. This paper compares the fault classification efficiency of standalone decision tree classifier, standalone rough set classifier with hybrid systems such as decision tree-fuzzy classifier and rough set-fuzzy classifier. The results obtained using standalone systems are compared with the performance of hybrid systems. It is observed that standalone systems outperform the hybrid systems.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Mining, Modelling and Management (IJDMMM):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook