Intelligent Bearing Diagnostics Using Wavelet Support Vector Machine

Abstract:

This paper deals with implementation of intelligent system for fault diagnostics of rolling element bearing. In this work, the proposed intelligent system was basically created using support vector machine (SVM) due to its excellent performance in classification task. Moreover, SVM was modified by introducing wavelet function as kernel for mapping input data into feature space. Input data were vibration signals acquired from bearings through standard data acquisition process. Statistical features were then calculated from bearing signals, and extraction of salient features was conducted using component analysis. Results of fault diagnostics are shown by observing classification of bearing conditions which gives plausible accuracy in testing of the proposed system.

• Periodical: Applied Mechanics and Materials (Volume 493)
• Pages: 337-342
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.493.337
• Online since: January 2014
• Authors: Achmad Widodo, I. Haryanto, T. Prahasto
• Keywords: Fault Diagnostics, Intelligent System, Rolling Element Bearing, Support Vector Machine (SVM)

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