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SETSCI - Volume 4 (6) (2019)
ISAS WINTER-2019 (ENS) - 4th International Symposium on Innovative Approaches in Engineering and Natural Sciences, Samsun, Turkey, Nov 22, 2019

Classification of Induction Motors by Fault Type with bidirectional Long-Short Term Memory Method
Ahmet Ali Süzen1*, Kıyas Kayaalp2
1Isparta University, Isparta, Turkey
2Isparta University, Isparta, Turkey
* Corresponding author:
Published Date: 2019-12-22   |   Page (s): 262-265   |    249     6

ABSTRACT It is important to determine the initial level of failures of induction motors used in many industrial applications. The sudden stops of the system can be prevented with the pre-detection of the fault. The experiment mechanism was established to detect mechanical unbalance and short circuit faults in the induction motors. Current values were measured and saved at fault time. As a result, 9.000 data were obtained consisting of 3 phase currents. In this study, a Long-Short Term Memory (LSTM) deep neural network has been developed that classification of induction motors by fault type. In the training of the neural network, 3 input parameters and 3 classification types of 1 output parameter are used. It was reserved for training 60% of data and 40% for testing the model in the dataset. As a result of the fault type classification with the LSTM model, 98.5% accuracy and 1.12 average absolute error value were obtained. It has been shown that the proposed bi-LSTM network can be used for fault detection of asynchronous motors.
KEYWORDS Classification, Deep Neural Network, Induction Motor, bi-LSTM, Type Fault
REFERENCES [1] Hassan, O. E., Amer, M., Abdelsalam, A. K., & Williams, B. W. (2018). Induction motor broken rotor bar fault detection techniques based on fault signature analysis–a review. IET Electric Power Applications, 12(7), 895-907.
[2] Sun, W., Shao, S., Zhao, R., Yan, R., Zhang, X., & Chen, X. (2016). A sparse auto-encoder-based deep neural network approach for induction motor faults classification. Measurement, 89, 171-178.
[3] Bessam, B., Menacer, A., Boumehraz, M., & Cherif, H. (2016). Detection of broken rotor bar faults in induction motor at low load using neural network. ISA transactions, 64, 241-246.
[4] Kayaalp, K., & Süzen, A. A. (2018). Derin Öğrenme ve Türkiye’deki Uygulamaları. IKSAD Publishing House.92p.
[5] Doğan, F., & Türkoğlu, İ. (2018). Derin Öğrenme Algoritmalarının Yaprak Sınıflandırma Başarımlarının Karşılaştırılması. Sakarya University Journal of Computer and Information Sciences, 1(1), 10-21.
[6] Graves and J. Schmidhuber, “Framewise phoneme classification with bidirectional LSTM and other neural network architectures,” Neural Networks, vol. 18, no. 5–6, pp. 602–610, Jul. 2005.
[7] Baccouche, M., F. Mamalet, C. Wolf, C. Garcia, and A. Baskurt, “Sequential Deep Learning for Human Action Recognition,” Springer, Berlin, Heidelberg, 2011, pp. 29–39.
[8] Kalchbrenner, N., E. Grefenstette, and P. Blunsom, “A Convolutional Neural Network for Modelling Sentences,” Apr. 2014.
[9] Ronen, R., Radu, M., Feuerstein, C., Yom-Tov, E., & Ahmadi, M. (2018). Microsoft malware classification challenge. arXiv preprint arXiv:1802.10135.
[10] Liu, Y., Sun, C., Lin, L., & Wang, X. (2016). Learning natural language inference using bidirectional LSTM model and inner-attention. arXiv preprint arXiv:1605.09090.
[11] Zhao, Z., Chen, W., Wu, X., Chen, P. C., & Liu, J. (2017). LSTM network: a deep learning approach for short-term traffic forecast. IET Intelligent Transport Systems, 11(2), 68-75.
[12] Zhang, L., Wang, S., & Liu, B. (2018). Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), e1253.
[13] Olah, Christopher. "Understanding LSTM Networks." Colah's Blog. Github, 27 Aug. 2015. Web. 04 May 2016.
[14] Khan, T., Alekhya, P., & Seshadrinath, J. (2018, September). Incipient Inter-turn Fault Diagnosis in Induction motors using CNN and LSTM based Methods. In 2018 IEEE Industry Applications Society Annual Meeting (IAS) (pp. 1-6). IEEE.
[15] Wang, Y., Shen, Y., Mao, S., Chen, X., & Zou, H. (2018). LASSO and LSTM Integrated Temporal Model for Short-Term Solar Intensity Forecasting. IEEE Internet of Things Journal, 6(2), 2933-2944.
[16] Housley, G., Lewis, S., Usman, A., Gordon, A. L., & Shaw, D. E. (2017). Accurate identification of hospital admissions from care homes; development and validation of an automated algorithm. Age and ageing, 47(3), 387-391.

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