Sugumaran V

Mechanical

School of Mechanical Engineering

Chennai Campus

Gaikwad Abhijit D

Abhijit D Gaikwad

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.

&nbsp;

&nbsp;

VIT University

Indian Journal of Science and Technology

Recent Patents on Signal Processing

Vibration Based Fault Diagnosis Study of an Automobile Brake System Using K Star (K*) Algorithm – A Statistical Approach

Hydraulic brakes are the most important components in automobile. It requires advanced supervision and fault diagnosis to improve the safety of passenger, reliability and economy. Condition monitoring is one of the major division through which the reliability of such components could be monitored. The condition of the brake components can be monitored by using the vibration characteristics which will reveal the condition of the brake systems. In this paper machine learning algorithm using vibration monitoring is proposed as a possible solution to this problem. From the hydraulic brake test set up, the vibration signals were acquired by using a piezoelectric transducer and data acquisition system. C4.5 decision tree algorithm was used to extract statistical features from vibration signals. Feature selection was also carried out. Since no much of methodologies are available to find the effective number of features for a given problem, a detailed study is needed to find the best possible number of features. Hence the effect of number of features was studied by using decision tree. The selected features were classified using C4.5 decision tree algorithm and Best first decision tree algorithm with pre pruning and post pruning techniques. The results are discussed and conclusions of the study are presented. © 2013 Elsevier Ltd. All rights reserved.

Measurement

Comparative study of decision tree classifier and best first tree classifier for fault diagnosis of automobile hydraulic brake system using statistical features

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.

Exploiting sound signals for fault diagnosis of bearings using decision tree

Mechanical Systems and Signal Processing

Vibration analysis of rotating machinery using time–frequency analysis and wavelet techniques

Expert Systems with Applications

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

Fault Diagnosis of Roller Bearings with Sound Signals using Wavelets and Decision Tree Algorithm

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