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Condition Monitoring in Roller Bearings using Cyclostationary Features

Publication Type : Conference Paper

Publisher : WCI

Source : WCI, pp 690-607, 2015 (Scopus)

Campus : Amritapuri, Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking

Department : Center for Computational Engineering and Networking (CEN), Electronics and Communication

Year : 2015

Abstract : Proper machine condition monitoring is really crucial for any industrial and mechanical systems. The efficiency of mechanical systems greatly relies on rotating components like shaft, bearing and rotor. This paper focuses on detecting different fault in the roller bearings by casting the problem as machine learning based pattern classification problem. The different bearing fault conditions considered are, bearing-good condition, bearing with inner race fault, bearing with outer race fault and bearing with inner and outer race fault. Earlier the statistical features of the vibration signals were used for the classification task. In this paper, the cyclostationary behavior of the vibration signals is exploited for the purpose. In the feature space the vibration signals are represented by cyclostationary feature vectors extracted from it. The features thus extracted were trained and tested using pattern classification algorithms like decision tree J48, Sequential Minimum Optimization (SMO) and Regularized Least Square (RLS) based classification and provides a comparison on accuracies of each method in detecting faults.

Cite this Research Publication : Sachin Kumar S, Neethu Mohan, Prabaharan P, KP Soman, Condition Monitoring in Roller Bearings using Cyclostationary Features, WCI, pp 690-607, 2015 (Scopus)

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