Fault Diagnosis for Induction Motor Using Patten Recognition Techniques
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概要
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Induction motors play a very important role in the safe and efficient operation of industrial plants and processes. The unexpected fault of motor causes many troubles such as the pause of overall process machinery as well as motors. In recent years, the fault detection and diagnosis of induction motors have been gaining more interests in the filed of highly reliable systems. For reliable fault diagnosis, it is extremely required in detecting and classifying the fault elements. There are some detection methods to identify the motor faults. Among the detection methods, the mainly used approaches are vibration monitoring and motor current signature analysis (MCSA). The vibration method is based on detecting vibration signal when motors happen to fault. This method, however, has some problems such as selection of reliable sensors and position attached on induction motors. For the fault diagnosis of three-phase induction motors, we set up an experimental unit and then develop a diagnosis algorithm based on pattern recognition. The experimental unit consists of induction motor drive and data acquisition module to obtain the fault signals. As the first step for diagnosis procedure, preprocessing is performed to make the acquired current simplified and normalized. To simplify the input data, three-phase currents are transformed into the magnitude of Concordia vector. As the next step, feature extraction is performed by PCA. Finally, we used the SVM classifier for fault detection of induction motor. To show the effectiveness, the proposed fault diagnostic system has been intensively tested with the various data acquired under the different electrical and mechanical faults with varying load.
- 日本知能情報ファジィ学会の論文
日本知能情報ファジィ学会 | 論文
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