Publication Type:

Journal Article


Expert Systems with Applications, Volume 38, Number 4, p.3819-3826 (2011)



Bearings (structural), Critical component, Decision trees, Decision-tree algorithm, Fault classification accuracy, Fault diagnosis, Faulty bearings, Faulty condition, Feature extraction, Kernel function, Machine element, Machinery, Mechanical systems, Mechanics, Multicomponents, Prominent features, Shaft and bearings, Statistical features, Support vector machines, Training and testing, Vectors, Vibration signal, Vibrations (mechanical)


<p>The shaft and bearing are the most critical components in rotating machinery. Majority of problems arise from faulty bearings in turn affect the shaft. The vibration signals are widely used to determine the condition of machine elements. The vibration signals are used to extract the features to identify the status of a machine. This paper presents the use of c-SVC and nu-SVC models of support vector machine (SVM) with four kernel functions for classification of faults using statistical features extracted from vibration signals under good and faulty conditions of rotational mechanical system. Decision tree algorithm was used to select the prominent features. These features were given as inputs for training and testing the c-SVC and nu-SVC model of SVM and their fault classification accuracies were compared. © 2010 Elsevier Ltd. All rights reserved.</p>


cited By (since 1996)17

Cite this Research Publication

Dr. Saimurugan M., Dr. K. I. Ramachandran, Sugumaran, V., and Dr. Sakthivel N.R., “Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine”, Expert Systems with Applications, vol. 38, pp. 3819-3826, 2011.