Cover
Vol. 22 No. 1 (2026)

Published: June 15, 2026

Pages: 311-323

Original Article

Speed Estimation of a Direct Current Motor Based on a Convolution Neural Network

Hilal Al-Libawy 1 Riyadh Toman Thahab 1 Hayder A. Abdulrahem 2
1 Department of Electrical Engineering, College of Engineering, University of Babylon, Babylon, Iraq
2 Department of Electrical and Electronics Engineering, College of Engineering, University of Thi-Qar, Thi-Qar, Iraq
History
  • Received: March 18, 2024
  • Revised: July 2, 2024
  • Accepted: July 5, 2024
  • Available Online: June 15, 2026
DOI

https://doi.org/10.37917/ijeee.22.1.29

Abstract

Electrical motors have been engaged in many residential, industrial and commercial applications. The speed of an electric motor is an essential output quantity which is needed in many processing systems. Therefore, estimating the speed of an electrical motor is an integral part in the hierarchy of operational and control process. In this work, a new speed estimation method is proposed which is based on a naturally occurring signal; the mechanical vibrations the body of the motor endure during operation. These vibration signals are measured in multi-axial dimension through accelerometer and gyroscope. Furthermore, the collected data is trained in a machine learning model. The model is used subsequently to estimate the speed of a self-excited direct current (DC) motor. Two approaches (offline and onboard) are followed to evaluate the fitness and the performance of the proposed method. The offline approach is performed using regression learner MATLAB toolbox and many algorithms are tested and results with different performance metrics are presented. The algorithm that yields best performance in terms of minimum Root Mean Square and maximum regression factor (R2) is selected as candidate for offline revolutions per minute (rpm) estimation. Results documents that with Gaussian process regression algorithm, estimations are obtained with a mean square error of 7 rpm and an R2 value of 1 which is considered a very satisfactory performance. The second approach is motor speed estimation in real time using vibration signals with deep learning model implemented on limited resources electronic board which is proposed for the first time to the best of our knowledge. The proposed method has been successfully implemented by low consumption resources from the selected board with 6.5 kb of ram and 91ms latency. Even with the limited resources, a rated speed estimate percentage error of 0.18% was recorded from real time results. Moreover, the proposed method is characterized by its simplicity, low technical requirements and eventually low cost of implementation. The aforementioned features make this method an attractive platform for speed estimation in many industrial applications.

Keywords

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