Support Vector Machine for Machine Fault Diagnosis and Prognosis

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概要

• 論文の詳細を見る

• This paper presents a survey of fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and development of SVM in machine fault diagnosis. Numerous methods have been developed based on intelligent system. However, the use of SVM for machine fault diagnosis is still rare. In addition, this paper introduces the feasibility of SVM based on regression (SVR) for machine prognosis system. The proposed method is addressed to predict the upcoming state of machine based on previous condition. The viability of developed system is evaluated by using trending data of low methane compressor acquired from condition monitoring routine. The results show that SVR has potential and promise for reliable and robust forecasting tool in machine prognosis system.

著者

• YANG Bo-Suk

  School of Mechanical Engineering, Pukyong National University

• Yang Bo-suk

  School Of Mechanical Engineering Pukong National University

• WIDODO Achmad

  School of Mechanical Engineering, Pukyong National University

関連論文

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• 206 FAULT DIAGNOSIS SYSTEM OF INDUCTION MOTORS USING FEATURE EXTRACTION, FEATURE SELECTION AND CLASSIFICATION ALGORITHM(General Session I)
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• Support Vector Machine for Machine Fault Diagnosis and Prognosis

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