Student Assistant Portal

There is no communication medium which is beneficent to all the people in the college through which they can share their ideas, information and have a personal account where they can save some stuff which is helpful in future. So our aim is to provide an application which provides a good interaction between all members of the college and is available for them anywhere. This is an application which provides a common solution for personal library, schedule our work and Forum. The project is meant for web based which allow the users to access it from anywhere as most of them carry smart phones with them. The proposed project is to help learn subject efficiently with connected to the university network : Faculty, Students, Management and other department

Fault classification of three phase induction motors using Bi-LSTM networks

Abstract The induction motors are back bone of the modern industry and play very important role in manufacturing and transportation sectors. The induction motor faults are mainly classified into internal faults such as inter turn short circuits , broken rotors and external faults such as over load, over voltage faults and asymmetry in supply voltage. The identification of type of fault is very important for safe operation and for preventing risk of machine failures. In this work, a Bidirectional Long Short Term memory networks (Bi-LSTM)-based machine learning methodology is proposed for classification of external faults of Induction Motors. The line voltages of the three phases and the three line currents are considered as the inputs to the Bi-LSTM network for identifying types of fault. Line voltage and line current data sets are considered for six different types of fault conditions. The six different conditions of the three phase induction motor are normal output (NO), overload (OL), over voltage (OV), under voltage (UV), Voltage unbalance (VUB) and single phasing (SP). The BI-LSTM network is trained using Adam optimization algorithm. The classification results are obtained with Bi-LSTM network are compared with LSTM networks to show the advantage of the proposed approach