Rajesh Kanna B

Computer Science

School of Computer Science and Engineering

Chennai Campus

Sugumaran V

Mechanical

School of Mechanical Engineering

S aravinth

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

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

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

Data Acquisition System and Signal Processing Technique for Bearing Fault Analysis

Journal of Physics: Conference Series

Feature Selection and Fault Classification of Reciprocating Compressors using a Genetic Algorithm and a Probabilistic Neural Network

2011 IEEE International Conference on System Engineering and Technology

An optimized fault diagnosis method for reciprocating air compressors based on SVM

Air Compressor Fault Diagnosis Through Vibration Signals using Statistical Features and J48 Algorithms

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