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.

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VIT University

Indian Journal of Science and Technology

Monoblock centrifugal pumps are employed in variety of critical engineering applications. Continuous monitoring of such machine component becomes essential in order to reduce the unnecessary break downs. At the outset, vibration based approaches are widely used to carry out the condition monitoring tasks. Particularly fuzzy logic, support vector machine (SVM) and artificial neural networks were employed for continuous monitoring and fault diagnosis. In the present study, the application of SVM algorithm in the field of fault diagnosis and condition monitoring is discussed. The continuous wavelet transforms were calculated for different families and at different levels. The computed transformation coefficients form the feature set for the classification of good and faulty conditions of the components of centrifugal pump. The classification accuracies of different continuous wavelet families at different levels were calculated and compared to find the best wavelet for the fault diagnosis of the monoblock centrifugal pump. © 2014 Karabuk University

Engineering Science and Technology, an International Journal

Fault diagnosis of monoblock centrifugal pump using SVM

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.

Measurement

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

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

Matrix Algorithms in MATLAB

Title Page

Applied Soft Computing

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

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

Journal	Indian Journal of Science and Technology
Publisher	Indian Society for Education and Environment
ISSN	0974-6846
Open Access	Yes