Sugumaran V

Mechanical

School of Mechanical Engineering

Chennai Campus

Rahul kumar sharma

Hemantha kumar

M amarnath

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

International Journal of Decision Support Systems

Bearing is one of vital transmission elements, finding numerous applications in small, medium and large machineries. Sound and vibration signals of a rotating machine contain the dynamic information about its operating condition. There are many articles in the literature reporting suitability of vibration signals for fault diagnosis applications; however, the transducer used for vibration signals (accelerometers) is costly. This prevents small scale industries and low cost equipments from using diagnostic tools on affordability ground. On the other hand, transducers used for acquiring sound signals are relatively low cost or/and affordable. Hence, there is a need for studying the use of sound signal for fault diagnosis applications. This paper uses acoustic signals (sound) acquired from near field area of bearings in good and simulated faulty conditions for the purpose of fault diagnosis through machine learning approach. The descriptive statistical features were extracted from sound signals and the important ones were selected using decision tree (dimensionality reduction). The selected features were then used for classification using C4.5 decision tree algorithm. The paper also discusses the effect of features, effect of various classifier parameters on classification accuracy. © 2012 Elsevier Ltd. All rights reserved.

Measurement

Exploiting sound signals for fault diagnosis of bearings using decision tree

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

Computers & Industrial Engineering

Automatic bearing fault diagnosis based on one-class ν-SVM

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

Advanced Materials Research

Design of Hydraulic Fault Diagnosis System Based on LabVIEW

A comparative study of naive Bayes classifier and Bayes net classifier for fault diagnosis of roller bearing using sound signal

Journal	International Journal of Decision Support Systems
Publisher	Inderscience Publishers
ISSN	2050-6988
Open Access	0