Vibration based fault diagnosis of monoblock centrifugal pump using decision tree

Authors:
N. R. Sakthivel;V. Sugumaran;S. Babudevasenapati
Affiliations:
Department of Mechanical Engineering, Amrita School of Engineering, Ettimadai, Coimbatore 641 105, India;Department of Mechanical Engineering, SRM University, Chennai, India;Department of Mechanical Engineering, Amrita School of Engineering, Ettimadai, Coimbatore 641 105, India
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

Citing 4
Cited 3

Artificial neural network approach for fault detection in rotary system

Applied Soft Computing
Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference

Expert Systems with Applications: An International Journal
A novel hybrid intelligent method based on C4.5 decision tree classifier and one-against-all approach for multi-class classification problems

Expert Systems with Applications: An International Journal
Improved use of continuous attributes in C4.5

Journal of Artificial Intelligence Research

Fault prognosis of mechanical components using on-line learning neural networks

ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Automatic rule learning using roughset for fuzzy classifier in fault categorization of mono-block centrifugal pump

Applied Soft Computing
A comparative study of Naïve Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis

Applied Soft Computing

Quantified Score

Hi-index	12.05

Visualization

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.