Deep Neural Network Based Fault Detection for Three-Phase Induction Motor
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Published
Jul 14, 2017
Sunghwan Cha
Simon Lee
Jun Jo
Yong Oh Lee
Jongwoon Hwang
Abstract
The three-phase induction motor is known as one of the most widely-used machine in the manufacturing industry. Electrical and mechanical fault of this machine is able to cause breaking down the plant facilities that leads significant productivity losses. In this paper, we will propose the fault detection method of induction motor by using deep neural network with measurement data of the electrical current signals to provide supervised classification of 2 types of
motor fault, rotor broken and stator conductor fault. We also studied the size of data-set in faulty state to train the model for fault detection.
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Keywords
PHM
References
Bonnett, Austin H., and George C. Soukup (1992) Cause and analysis of stator and rotor failures in threephase squirrel-cage induction motors, IEEE Transactions on Industry Applications 28.4: 921-937.
Benbouzid, Mohamed El Hachemi, and Gerald B. Kliman. (2003) What stator current processing-based technique to use for induction motor rotor faults diagnosis?, IEEE Transactions on Energy Conversion 18.2: 238-244.
Wang, Wilson, and Ofelia Antonia Jianu. (2010) A smart sensing unit for vibration measurement and monitoring, IEEE/ASME transactions on mechatronics 15.1: 70-78.
Al-Dossary, Saad, RI Raja Hamzah, and David Mba (2009) Observations of changes in acoustic emission waveform for varying seeded defect sizes in a rolling element bearing, Applied acoustics 70.1 (2009): 58-81.
Chen, Shuo, and Rastko Živanović. (2010) Modelling and simulation of stator and rotor fault conditions in induction machines for testing fault diagnostic techniques, European Transactions on Electrical Power 20.5 611-629.
Zarei, J., Tajeddini, M. A., & Karimi, H. R. (2014). Vibration analysis for bearing fault detection and classification using an intelligent filter. Mechatronics, 24(2), 151-157.
Konar, P., & Chattopadhyay, P. (2011). Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs). Applied Soft Computing, 11(6), 4203-4211.
Ertunc, H. M., Ocak, H., & Aliustaoglu, C. (2013). ANN-and ANFIS-based multi-staged decision algorithm for the detection and diagnosis of bearing faults. Neural Computing and Applications, 22(1), 435-446.
Benbouzid, Mohamed El Hachemi, and Gerald B. Kliman. (2003) What stator current processing-based technique to use for induction motor rotor faults diagnosis?, IEEE Transactions on Energy Conversion 18.2: 238-244.
Wang, Wilson, and Ofelia Antonia Jianu. (2010) A smart sensing unit for vibration measurement and monitoring, IEEE/ASME transactions on mechatronics 15.1: 70-78.
Al-Dossary, Saad, RI Raja Hamzah, and David Mba (2009) Observations of changes in acoustic emission waveform for varying seeded defect sizes in a rolling element bearing, Applied acoustics 70.1 (2009): 58-81.
Chen, Shuo, and Rastko Živanović. (2010) Modelling and simulation of stator and rotor fault conditions in induction machines for testing fault diagnostic techniques, European Transactions on Electrical Power 20.5 611-629.
Zarei, J., Tajeddini, M. A., & Karimi, H. R. (2014). Vibration analysis for bearing fault detection and classification using an intelligent filter. Mechatronics, 24(2), 151-157.
Konar, P., & Chattopadhyay, P. (2011). Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs). Applied Soft Computing, 11(6), 4203-4211.
Ertunc, H. M., Ocak, H., & Aliustaoglu, C. (2013). ANN-and ANFIS-based multi-staged decision algorithm for the detection and diagnosis of bearing faults. Neural Computing and Applications, 22(1), 435-446.
Section
Regular Session Papers