Sugumaran V

Mechanical

School of Mechanical Engineering

Chennai Campus

Ayush kimothi

Ameet singh

M amarnath

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 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

Rule learning based approach to fault detection and diagnosis is becoming very popular, mainly due to their high accuracy when compared to older statistical methods. Fault detection and diagnosis of various mechanical components of centrifugal pump is essential to increase the productivity and reduce the breakdowns. This paper presents the use of rough sets to generate the rules from statistical features extracted from vibration signals under good and faulty conditions of a centrifugal pump. A fuzzy inference system (FIS) is built using rough set rules and tested using test data. The effect of different types of membership functions on the FIS performance is also presented. Finally, the performance of this classifier is compared to that of a fuzzy-antminer classifier and to multi-layer perceptron (MLP) based classifiers. © 2011 Elsevier B.V. All rights reserved.

Applied Soft Computing

Automatic rule learning using roughset for fuzzy classifier in fault categorization of mono-block centrifugal pump

Shock and Vibration

Feature Extraction Using Discrete Wavelet Transform for Gear Fault Diagnosis of Wind Turbine Gearbox

Improving Classification Accuracy based on Random Forest Model through Weighted Sampling for Noisy Data with Linear Decision Boundary

International Journal of Hybrid Information Technology

Gear Box Fault Diagnosis using Hilbert Transform and Study on Classification of Features by Support Vector Machine

Fault Diagnosis of Helical Gearbox through Vibration Signals using Wavelet Features, J48 Decision Tree and Random Forest Classifiers

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