To search, Click below search items.


All Published Papers Search Service


LSTM based Supply Imbalance Detection and Identification in Loaded Three Phase Induction Motors


Majid Hussain, Fayaz Ahmed Memon, ,Umair Saeed, Babar Rustum, Kelash Kanwar, and Abdul Rafay Khatri


Vol. 23  No. 1  pp. 147-152


Mostly in motor fault detection the instantaneous values 3 axis vibration and 3phase current in time domain are acquired and converted to frequency domain. Vibrations are more useful in diagnosing the mechanical faults and motor current has remained more useful in electrical fault diagnosis. With having some experience and knowledge on the behavior of acquired data the electrical and mechanical faults are diagnosed through signal processing techniques or combine machine learning and signal processing techniques. In this paper, a single-layer LSTM based condition monitoring system is proposed in which the instantaneous values of three phased motor current are firstly acquired in simulated motor in in health and supply imbalance conditions in each of three stator currents. The acquired three phase current in time domain is then used to train a LSTM network, which can identify the type of fault in electrical supply of motor and phase in which the fault has occurred. Experimental results shows that the proposed single layer LSTM algorithm can identify the electrical supply faults and phase of fault with an average accuracy of 88% based on the three phase stator current as raw data without any processing or feature extraction.


Condition Monitoring, Motor fault diagnosis, Electrical faults, LSTM