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Fault Diagnosis of PMSM Using Artificial Neural Network

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Faults in engineering systems are difficult to avoid and may lead to degraded performance, malfunctions or failures. In complex systems, a fault possesses the potential to impact the entire system behavior. In this paper, a fault detection technique for Permanent Magnet Synchronous Motor is proposed for detecting different type of faults. The proposal uses an artificial neural network to detect faults that develop gradually in the system. The Artificial Neural Network is trained using Levenberg-Marquardt algorithm and a Discrete Wavelet Transform based feature extraction block for Permanent magnet synchronous motor drive system is employed. The fault detection system classifies four of the most common and highly probable faults such as inverter short circuit, inverter open circuit, winding short circuit and winding open circuit. The proposed approach suggests that with a robust learning approach, a diagnostic system can be trained based on the machine parameters, which can lead to a correct identification of faults over a wide operating region.
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Discrete Wavelet Transform; Fault Diagnosis; Feature Extraction; Levenberg-Marquardt Algorithm; Open Circuit Fault; Permanent Magnet Synchronous Motor; Short Circuit Fault

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