206 FAULT DIAGNOSIS SYSTEM OF INDUCTION MOTORS USING FEATURE EXTRACTION, FEATURE SELECTION AND CLASSIFICATION ALGORITHM(General Session I)
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概要
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This paper proposes a fault diagnosis system for induction motors which integrates principal component analysis (PCA), genetic algorithm (GA) and artificial neural network (ANN). Vibration signals and stator current signals are measured as the fault diagnosis media. Many sensors result in many features to ANN. In order to avoid the curse of dimensionality phenomenon and improve the classification rate, PCA and GA are employed to reduce the feature dimensionality of the measured data. PCA removes the relative features, and extracts the principal components (PCs) of the original feature data set which contribute up to 99% of the data total variance. Then the irrelative features after PCA are selected by GA to find better feature subset as inputs to the network under a few population and generations. GA is also used to optimize the ANN structure in that the selected PCs feature subset is evaluated by it. The efficiency of the proposed system is validated by comparison of other three systems: ANN only, ANN with PCA and ANN with GA. The classification success rate for the ANN with PCA and GA was 100% for validation, while the rates of ANN only, ANN with PCA and ANN with GA were 83.33%, 86.67% and 98.89%, respectively.
- 一般社団法人日本機械学会の論文
- 2005-05-31
著者
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YANG Bo-Suk
School of Mechanical Engineering, Pukyong National University
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Son Jong-duk
School Of Mechanical Engineering Pukyong National University
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Han Tian
School of Mechanical Engineering, Pukyong National University
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Yang Bo-suk
School Of Mechanical Engineering Pukong National University
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Han Tian
School Of Mechanical Engineering Pukyong National University
関連論文
- Defect Diagnostics of Rolling Element Bearing Using Fuzzy Dichotomy Technique
- 206 FAULT DIAGNOSIS SYSTEM OF INDUCTION MOTORS USING FEATURE EXTRACTION, FEATURE SELECTION AND CLASSIFICATION ALGORITHM(General Session I)
- Fault Diagnosis System of Induction Motors Using Feature Extraction, Feature Selection and Classification Algorithm(Advanced Technology of Vibration and Sound)
- Support Vector Machine for Machine Fault Diagnosis and Prognosis