Real-time self-complement system of fault diagnosis for induction motor using machine learning and IoT Technique

被引：0

作者：

Choi D.-J. ^{[2
]}

Ha J.-H. ^{[2
]}

Hong S.-K. ^{[1
]}

机构：

[1] Dept. of Digital Control Engineering, Hoseo University

[2] Dept. of Information Control Engineering, Hoseo University

来源：

Transactions of the Korean Institute of Electrical Engineers | 2019年 / 68卷 / 05期

关键词：

Deep learning; Diagnosis; Iot; Real-time; Self-complement;

D O I：

10.5370/KIEE.2019.68.5.662

中图分类号：

学科分类号：

摘要：

Existing fault diagnosis using deep Learning, has experimented by collecting data from a controlled environment. However, it is not easy to diagnose motor faults in various environments, becuase input data are measured with various disturbances together. For this reason, in this paper, the verification and learning process are separated and used in each system so that motor data of various environments can be considered. In the verification process, a data preprocessing process is added to verify and collect necessary data. A CNN-based in-depth learning algorithm is implemented and data is stored in real time. Since the data is re-learned based on the collected data, a model considering both existing features and newly input data is created. Even the continuous disturbance is also used as learning data, it is easier to cope with disturbance than the conventional method. As a result, a system applicable to an industrial field is proposed considering various environments. © 2019 Korean Institute of Electrical Engineers. All rights reserved.

引用

页码：662 / 669

页数：7

共 10 条

[1]

Feng Z., Zuo M.J., Vibration signal models for fault diagnosis of planetary gearboxes, Journal of Sound and Vibration, 331, 22, pp. 1939-4919, (2012)

[2]

Lei Y., Jia F., Lin J., Xing S., Ding S.X., An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data, IEEE Trans. Ind. Electron, 63, 5, pp. 3137-3147, (2016)

[3]

Sun W., Zhao R., Yan R., Shao S., Chen X., Convolutional Discriminative Feature Learning for Induction Motor Fault Diagnosis, IEEE Trans. On Industrial Informatics, 13, 3, pp. 1350-1359, (2017)

[4]

Choi D.-J., Han J.-H., Kim H.-S., Choi M.C., Kwon and Sun-Ki Hong, "study on Motor Fault Diagnosis Considering Noise Disturbance Based on Machine Learning, KIEE EMECS Falling Conference, pp. 29-30, (2018)

[5]

Han J.-H., Choi D.-J., Hong S.-K., Study on Development of Deep Learning Fault Diagnosis Algorithm Considering Induction Motor Speed and Load Condition, Trans. On KIEE, 68, 3, pp. 1-7, (2019)

[6]

Wen L., Li X., Gao L., Zhang Y., A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method, IEEE Transactions on Industrial Electronics, 65, 7, pp. 5990-5996, (2018)

[7]

Simonyan K., Zisserman A., VERY DEEP CONVOLUTIONAL NETWORKS for LARGE-SCALE IMAGE RECOGNITION, Computer Vision and Pattern Recognition, (2015)

[8]

Krizhevsky A., Sutskever I., Hinton G., Imagenet classification with deep convolutional neural networks, Advancesin Neural Information Processing Systems, (2012)

[9]

Kim D.J., Implementation of Multi Channel Network Platform based Augmented Reality Facial Emotion Sticker using Deep Learning, Journal of Digital Contents Society, 19, 7, pp. 1349-1355, (2018)

[10]

Gubbi J., Buyya R., Marusic S., Palaniswami M., Internet of Things (IoT): A vision, architectural elements, and future directions, Future Gener. Comput. Syst, 29, 7, pp. 1645-1660, (2013)

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