Speed Estimation of a Direct Current Motor Based on a Convolution Neural Network - Al-Libawy, Hilal; Toman Thahab, Riyadh; A. Abdulrahem, Hayder (Iraqi Journal for Electrical and Electronic Engineering, 2026)
Electrical motors have been engaged in many residential, industrial and commercial applications. The speed of anelectric motor is an essential output quantity which is needed in many processing systems. Therefore, estimating thespeed of an electrical motor is an integral part in the hierarchy of operational and control process. In this work, a newspeed estimation method is proposed which is based on a naturally occurring signal; the mechanical vibrations thebody of the motor endure during operation. These vibration signals are measured in multi-axial dimension throughaccelerometer and gyroscope. Furthermore, the collected data is trained in a machine learning model. The model is usedsubsequently to estimate the speed of a self-excited direct current (DC) motor. Two approaches (offline and onboard) arefollowed to evaluate the fitness and the performance of the proposed method. The offline approach is performed usingregression learner MATLAB toolbox and many algorithms are tested and results with different performance metrics arepresented. The algorithm that yields best performance in terms of minimum Root Mean Square and maximum regressionfactor (R2) is selected as candidate for offline revolutions per minute (rpm) estimation. Results documents that withGaussian process regression algorithm, estimations are obtained with a mean square error of 7 rpm and an R2 value of 1which is considered a very satisfactory performance. The second approach is motor speed estimation in real time usingvibration signals with deep learning model implemented on limited resources electronic board which is proposed for thefirst time to the best of our knowledge. The proposed method has been successfully implemented by low consumptionresources from the selected board with 6.5 kb of ram and 91ms latency. Even with the limited resources, a rated speedestimate percentage error of 0.18% was recorded from real time results. Moreover, the proposed method is characterizedby its simplicity, low technical requirements and eventually low cost of implementation. The aforementioned featuresmake this method an attractive platform for speed estimation in many industrial applications.