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• W4378222473 abstract "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." @default.
• W4378222473 created "2023-05-26" @default.
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• W4378222473 date "2023-05-25" @default.
• W4378222473 modified "2023-10-14" @default.
• W4378222473 title "Fault classification of three phase induction motors using Bi-LSTM networks" @default.
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• W4378222473 doi "https://doi.org/10.1186/s43067-023-00098-x" @default.
• W4378222473 hasPublicationYear "2023" @default.
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