Abstract
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.
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Banerjee, T.P., Das, S. (2011). Statistical and Fusion Based Hybrid Approach for Fault Signal Classification in Electromechanical System. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2011. Lecture Notes in Computer Science, vol 7077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27242-4_33
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DOI: https://doi.org/10.1007/978-3-642-27242-4_33
Publisher Name: Springer, Berlin, Heidelberg
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