AI-Based Fault Detection in Electric Vehicle Motors Using FFT-Based Vibration Analysis and Edge AI

N. Mohan; Vikunth M. Athreya; Aradhana Panda; Kiran Y. Rampur; M. Apeksha

doi:10.2991/978-94-6239-789-7_11

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AI-Based Fault Detection in Electric Vehicle Motors Using FFT-Based Vibration Analysis and Edge AI

Authors

N. Mohan¹, Vikunth M. Athreya²^{, *}, Aradhana Panda², Kiran Y. Rampur², M. Apeksha²

¹JSS Science And Technology University, Mysuru, India

²Dept. Electrical and Electronics Engineering, JSS Science and Technology University, Mysuru, India

^*Corresponding author. Email: vikunthmathreya@gmail.com

Corresponding Author

Vikunth M. Athreya

Available Online 8 October 2026.

DOI: 10.2991/978-94-6239-789-7_11 How to use a DOI?
Keywords: Electric vehicle motors; vibration analysis; Fast Fourier Transform (FFT); Edge AI; Internet of Things (IoT); machine learning
Abstract: In order to ensure the safe and efficient working of electric vehicle motors, it is imperative to ensure the reliable and timely identification of defects. For this purpose, the Internet of Things, Fast Fourier Transform, and Edge Artificial Intelligence have been employed in the defect identification system proposed in this paper. A variety of sensors have been employed in the defect identification system, including an infrared sensor for speed measurement, DS18B20 for temperature measurement, INA219 for current measurement, and MPU6050 for vibration measurement. In order to detect mechanical defects like shaft misalignment, vibration is measured, and Fast Fourier transform (FFT) is applied for obtaining frequency domain values. A lightweight machine learning model is applied for defect classification using ESP32-S3. The proposed technique is applicable for electric vehicles as experimental results show that defects such as misalignment, overcurrent, and overheating can be identified.
Copyright: © 2026 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of theInternational Conference on Innovation and Practices in Artificial Intelligence (ICIPAI 2026)
Series: Advances in Intelligent Systems Research
Publication Date: 8 October 2026
ISBN: 978-94-6239-789-7
ISSN: 1951-6851
DOI: 10.2991/978-94-6239-789-7_11 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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ris enw bib

TY  - CONF
AU  - N. Mohan
AU  - Vikunth M. Athreya
AU  - Aradhana Panda
AU  - Kiran Y. Rampur
AU  - M. Apeksha
PY  - 2026
DA  - 2026/10/08
TI  - AI-Based Fault Detection in Electric Vehicle Motors Using FFT-Based Vibration Analysis and Edge AI
BT  - Proceedings of theInternational Conference on Innovation and Practices in Artificial Intelligence (ICIPAI 2026)
PB  - Atlantis Press
SP  - 143
EP  - 161
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-789-7_11
DO  - 10.2991/978-94-6239-789-7_11
ID  - Mohan2026
ER  -

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