Publication Type:

Journal Article

Source:

Expert Systems with Applications, Volume 37, Number 6, p.4040-4049 (2010)

URL:

http://www.scopus.com/inward/record.url?eid=2-s2.0-77249155059&partnerID=40&md5=c8cc09ff6a4cf7d272a962240f95619c

Keywords:

Artificial Neural Network, C4.5 algorithm, C4.5 decision tree algorithm, Centrifugal pumps, Condition monitoring, Continuous monitoring, Decision trees, Failure analysis, Fault diagnosis, Faulty condition, Fuzzy logic, Fuzzy neural networks, Gaining momentum, Hydraulic machinery, Machine-learning, Pumping plants, Pumps, Statistical features, Vibration analysis, Vibration signal

Abstract:

Monoblock centrifugal pumps are widely used in a variety of applications. In many applications the role of monoblock centrifugal pump is critical and condition monitoring is essential. Vibration based continuous monitoring and analysis using machine learning approaches are gaining momentum. Particularly artificial neural networks, fuzzy logic were employed for continuous monitoring and fault diagnosis. This paper presents the use of C4.5 decision tree algorithm for fault diagnosis through statistical feature extracted from vibration signals of good and faulty conditions.

Notes:

cited By (since 1996)13

Cite this Research Publication

Dr. Sakthivel N.R., Sugumaran, Vb, and Babudevasenapati, Sa, “Vibration based fault diagnosis of monoblock centrifugal pump using decision tree”, Expert Systems with Applications, vol. 37, pp. 4040-4049, 2010.

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