Statistical and Fusion Based Hybrid Approach for Fault Signal Classification in Electromechanical System
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Motor fault diagnostics in dynamic condition is a typical multi-sensor fusion problem. It involves the use of multi-sensor information such as vibration, sound, current, voltage and temperature, to detect and identify motor faults. According to our experiments in BLDC motor controller results, the system has potential to serve as an intelligent fault diagnosis system in other hard real time system application. To make the system more robust we make the controller more adaptive that give the system response more reliable by the multisensory fusion techniques. We introduce a hybrid model based new methods and evaluate the performance of the proposed information fusion system. Finally, we report the efficiency of this system in dealing with controller stabitility and its nonlinear information that may arise among the sensors.
KeywordsMotor diagnosis Information fusion Sensor fusion Support Vector Machine Sort Term Fourier Transform Brash less Direct Current Motor signal classification Fault Classifier
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