The fault diagnosis problem is conceived as a classification problem. In the present study, vibration signals are used for fault diagnosis of centrifugal pumps using wavelet analysis. Rough set theory is applied to generate the rules from the vibration signals. Based on the strength of the rules the faults are identified. The different faults considered for this study are: pump at good condition, cavitation, pump with faulty impeller, pump with faulty bearing and pump with both faulty bearing and impeller. However, the classification accuracy is based on the strength and number of rules generated using rough set theory. Wavelet features are computed using Discrete Wavelet Transform (DWT) from the vibration signals and rules are generated using rough sets and classified using fuzzy logic. The results are presented in the form of confusion matrix which shows the classification capability of wavelet features with rough set and fuzzy logic for fault diagnosis of monoblock centrifugal pump. © 2013 The Authors. Published by Elsevier Ltd. All rights reserved.

Muralidharan B

Department of Manufacturing Engineering

School of Mechanical Engineering

Vellore Campus

Sugumaran V

Mechanical

Chennai Campus

Fulltext

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

&nbsp;

&nbsp;

VIT University

Measurement

Indian Journal of Science and Technology

A Comparative study between Support Vector Machine (SVM) and Extreme Learning Machine (ELM) for Fault Detection in Pumps

Wind Turbine Blade Fault Diagnosis Using Vibration Signals through Decision Tree Algorithm

Monoblock centrifugal pumps are employed in variety of critical engineering applications. Continuous monitoring of such machines becomes essential in order to reduce the unnecessary break downs. Vibration based approaches are widely used to carry out the condition monitoring tasks. Decision tree, fuzzy logic, support vector machine and artificial neural networks are some of the classification algorithms employed for condition monitoring and fault diagnosis. In the present study, fault discriminating capability of wavelets in its continuous form with the application of J48 algorithm is analyzed. Vibration signals are extracted from the experimental setup. The continuous wavelet transform (CWT) is calculated for different families and at different levels which form the feature set. The features are then fed as an input to the classifier (J48 algorithm, a WEKA implementation) and the classification accuracies are calculated. Then, the results are validated to find classification capability of CWT features for monoblock centrifugal pump. The different faults considered for this study are cavitation (CAV), impeller fault, bearing fault (FB) and both bearing and impeller fault. © 2012 Elsevier Ltd. All rights reserved.

Feature extraction using wavelets and classification through decision tree algorithm for fault diagnosis of mono-block centrifugal pump

Monoblock centrifugal pumps play an important role in a variety of engineering applications such as in the food industry, in wastewater treatment plants, in agriculture, in the oil and gas industry, in the paper and pulp industry, and others. Condition monitoring of the various mechanical components of centrifugal pumps becomes essential for increasing productivity and reducing the number of breakdowns. Vibration-based continuous monitoring and analysis using machine learning approaches are gaining momentum. Particularly, artificial neural networks and fuzzy logic have been employed for continuous monitoring and fault diagnosis. This article presents the use of the J48 algorithm for fault diagnosis through discrete wavelet features extracted from vibration signals of good and faulty conditions of the components of a centrifugal pump. The classification accuracies of different discrete wavelet families were calculated and compared in order to find the best wavelet for the fault diagnosis of the centrifugal pump. © 2013 Copyright Taylor and Francis Group, LLC.

Applied Artificial Intelligence

SELECTION OF DISCRETE WAVELETS FOR FAULT DIAGNOSIS OF MONOBLOCK CENTRIFUGAL PUMP USING THE J48 ALGORITHM

Applied Soft Computing

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

Expert Systems with Applications

Fault diagnosis of roller bearing using fuzzy classifier and histogram features with focus on automatic rule learning

Mechanical Systems and Signal Processing

Comparison of decision tree-fuzzy and rough set-fuzzy methods for fault categorization of mono-block centrifugal pump

Rough set based rule learning and fuzzy classification of wavelet features for fault diagnosis of monoblock centrifugal pump

Journal	Data powered by TypesetMeasurement
Publisher	Data powered by TypesetElsevier BV
ISSN	0263-2241
Open Access	No