Fault Diagnosis of Induction Motor Using Convolutional Neural Network
Source abstract
Abstract
Induction motors are among the most important components of modern machinery and industrial equipment. Therefore, it is necessary to develop a fault diagnosis system that detects the operating conditions of and faults in induction motors early. This paper presents an induction motor fault diagnosis system based on a CNN (convolutional neural network) model. In the proposed method, vibration signal data are obtained from the induction motor experimental environment, and these values are input into the CNN. Then, the CNN performs fault diagnosis. In this study, fault diagnosis of an induction motor is performed in three states, namely, normal, rotor fault, and bearing fault. In addition, a GUI (graphical user interface) for the proposed fault diagnosis system is presented. The experimental results confirm that the proposed method is suitable for diagnosing rotor and bearing faults of induction motors.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Fault Diagnosis Techniques · Fault Detection and Control Systems · Engineering Diagnostics and Reliability
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electronics Engineering, Kyungpook National University, Daegu 41566, Korea
Funding
National Research Foundation of Korea (NRF) · BK21 Plus project funded by the Ministry of Education
References (21)
Show source-provided references
1. Fault Diagnosis System Development of Induction Motors Using Discrete Wavelet Transform and Neural Network
2. Lee, I.S. (2011, January 9–14). Fault Diagnosis of Induction Motors Using Discrete Wavelet Transform and Artificial Neural Network. Proceedings of the International Conference on Human-Computer Interaction, Orlando, FL, USA.10.1007/978-3-642-22098-2_102 ↗
3. Vas, P. (1993). Parameter Estimation, Condition Monitoring, and Diagnosis of Electrical Machines, Oxford University Press. Oxford Science.10.1093/oso/9780198593751.001.0001 ↗
4. Neural-network-based motor rolling bearing fault diagnosis10.1109/41.873214 ↗
5. Motor bearing damage detection using stator current monitoring10.1109/28.475697 ↗
6. An unsupervised, on-line system for induction motor fault detection using stator current monitoring10.1109/28.475698 ↗
7. Current signature analysis of induction motor mechanical faults by wavelet packet decomposition10.1109/TIE.2003.819682 ↗
8. Experimental study for sensorless broken bar detection in induction motors10.1016/j.enconman.2007.06.030 ↗
9. Fourier transform and image processing for automatic detection of broken rotor bars in induction motors10.1088/1361-6501/aad3aa ↗
10. Acoustic based fault diagnosis of three-phase induction motor10.1016/j.apacoust.2018.03.010 ↗
11. Diagnosis of the three-phase induction motor using thermal imaging10.1016/j.infrared.2016.12.003 ↗
12. Learning methodology for failure detection and accommodation10.1109/37.387613 ↗
13. Hopfield/ART-1 neural network-based fault detection and isolation10.1109/72.329685 ↗
14. Multiple faults diagnosis of a linear system using ART2 neural networks
15. Diagnostic system development for state monitoring of induction motor and oil level in press process system
16. Agarap, A.F. (2017). An architecture combining convolutional neural network (CNN) and support vector machine (SVM) for image classification. arXiv.
17. Convolutional discriminative feature learning for induction motor fault diagnosis10.1109/TII.2017.2672988 ↗
18. Dasgupta, A., and Singh, S. (2017, January 18–21). A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation. Proceedings of the 2017 IEEE 14th International Symposium on Biomedical Imaging, Melbourne, Australia.10.1109/ISBI.2017.7950512 ↗
19. Deep learning and its applications to machine health monitoring10.1016/j.ymssp.2018.05.050 ↗
20. Tao, S., Zhang, T., Yang, J., Wang, X., and Lu, W. (2015, January 28–30). Bearing fault diagnosis method based on stacked autoencoder and softmax regression. Proceedings of the 2015 34th Chinese Control Conference, Hangzhou, China.10.1109/ChiCC.2015.7260634 ↗
21. Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude