Application of DempsterShafer theory in fault diagnosis of induction motors using vibration and current signals
This paper presents an approach for the fault diagnosis in induction motors by using DempsterShafer theory. Features are extracted from motor stator current and vibration signals and with reducing data transfers. The technique makes it possible for on-line application. Neural network is trained and tested by the selected features of the measured data. The fusion of classification results from vibration and current classifiers increases the diagnostic accuracy. The efficiency of the proposed system is demonstrated by detecting motor electrical and mechanical faults originated from the induction motors. The results of the test confirm that the proposed system has potential for real-time applications.
dempstershafer theory fault diagnosis induction motor vibration signal neural network data fusion condition monitoring stator current signal