Realtime Non-invasive Fault Diagnosis of Three-phase Induction Motor

Authors

  • Van Khanh Nguyen Can Tho University, Vietnam
  • Vy Khang Tran Automation and Control Engineering, Can Tho University, Vietnam
  • Minh Khai Nguyen Automation and Control Engineering, Can Tho University, Vietnam
  • Van To Em Thach Automation and Control Engineering, Can Tho University, Vietnam
  • Tran Lam Hai Pham Can Tho University, Vietnam
  • Chi Ngon Nguyen Can Tho University, Vietnam

Corressponding author's email:

vankhanh@ctu.edu.vn

DOI:

https://doi.org/10.54644/jte.72B.2022.1231

Keywords:

Non-invasive fault diagnosis, Spectrogram, esp32, deep learning network, embedded system

Abstract

The objective of this paper is to apply deep learning network running on an embedded system platform to diagnose faults of a three-phase electric motor by a non-contact method based on operating motor noise. To accomplish this, at first, deep learning network should be designed and trained on a computer, and then converted to an equivalent network to run on the embedded system. The network input data is a two-dimension spectrogram image of the noise emitted by the motor in four main cases, including normal operation, phase shift, phase loss and bearing failure. The execution time and accuracy of these deep learning network structures will be deployed on three microcontrollers including ESP32, ESP32-C3 and nRF52840 to determine the suitable embedded platform and network structure for real-time running. Experimental results show that the proposed deep learning network models could diagnose the faults well on both computer and embedded platform with the highest accuracies are 99,7% and 99,3%, respectively. In particular, the preliminary results are remarkable with the recognition time and accuracy at 1,7 seconds and 72%, respectively associated with the proposed deep learning network on realtime embedded system performance.

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Author Biographies

Van Khanh Nguyen, Can Tho University, Vietnam

Nguyen Van Khanh received his master degree from Ho Chi Minh University of Technology, Vietnam in 2014 and Doctor of Engineering degree from Tokyo University of Marine Science and Technology, Japan in 2020.

Since 2007,  he  has  been  a  lecturer  at Department of Automation Technology, College of Engineering Technology, Can Tho University. His research interests concentrate on embedded systems, AIoT- and IoT-based applications in environmental and agricultural control.

Vy Khang Tran, Automation and Control Engineering, Can Tho University, Vietnam

Tran Vy Khang is a B.S. degree student in Automation and Control Engineering of the Department of Automation Technology, College of Engineering, Can Tho University, Vietnam. He will graduate his B.S. degree at the end of December 2022. Email tranvykhang1906@gmail.com, Contact phone 0706950015.

Minh Khai Nguyen, Automation and Control Engineering, Can Tho University, Vietnam

Nguyen Minh Khai is a B.S. degree student in Automation and Control Engineering of the Department of Automation Technology, College of Engineering, Can Tho University, Vietnam. He will graduate his B.S. degree at the end of December 2022. Email nguyenminhkhai.070500@gmail.com, Contact phone 03698463651.

Van To Em Thach, Automation and Control Engineering, Can Tho University, Vietnam

Thach Van To Em is a B.S. degree student in Automation and Control Engineering of the Department of Automation Technology, College of Engineering, Can Tho University, Vietnam. He will graduate his B.S. degree at the end of September 2022. Email toem2704@gmail.com, Contact phone 0868080442

Tran Lam Hai Pham, Can Tho University, Vietnam

Pham Tran Lam Hai received his master degree from University of South Australia (UniSA) in 2010.

Since 2012, he has been a lecturer at Department of Automation Technology, College of Engineering Technology, Can Tho University. His research interests focus on Bistatic LIDAR system for gas measurement in environmental and agricultural applications.

Chi Ngon Nguyen, Can Tho University, Vietnam

Chi-Ngon Nguyen received B.S. and M.S. degrees in Electronic Engineering from Can Tho Universityand the National University, Ho Chi Minh City University of Technology, Vietnam, in 1996 and 2001, respectively. The degree of Ph.D. in Control Engineering was awarded by the University of Rostock, Germany, in 2007.

Since 1996, he has worked at the Can Tho University. He is an associate professor in automation at Department of Automation Technology, and former dean of the College of Engineering at the Can Tho University. Currently, he is a Vice Chairman of the Board of Trustee of Can Tho University.

His  research  interests  are  intelligent  control,  medical  control,  pattern  recognition,  classifications,  speech recognition, computer vision and agricultural automation

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Published

28-10-2022

How to Cite

[1]
V. K. Nguyen, V. K. Tran, M. K. Nguyen, V. T. E. Thach, T. L. H. Pham, and C. N. Nguyen, “Realtime Non-invasive Fault Diagnosis of Three-phase Induction Motor”, JTE, vol. 17, no. Special Issue 03, pp. 1–11, Oct. 2022.

Issue

Vol. 17 No. Special Issue 03 (2022)

Section

Research Article

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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