Sensorless Coil Temperature Measurements using Neural Networks for Voltage Control

Abstract

Voltage and current measurement data based deep learning as a method to conduct sensorless coil temperature prediction of an embedded linear induction actuator is being proposed and validated in this work. Generated numerical data from Finite Element field simulations are used to train a neural network which in turn predicts temperatures at non-accessible places e.g. at an embedded coil. The network is demonstrated and the comparison to experimental data shows the potential of virtual sensing. Even though the number of physical sensors have increased enormously in the last decades, the measurement of desired temperatures at certain locations is limited by accessibility and by the application itself, for example, if a coil is used as a moving part in an actuator. This work proposes an indirect method based on measurable quantities in the device, such as voltage and current, to quantify precisely temperatures and hot spots in sensitive parts of the device. As high temperatures can have a huge effect on the device's performance, a controllable voltage to compensate the performance reduction instantaneously is desired. Applications based on the principle of an inductive linear actuator show a strong performance dependency on the temperature of the conducting material or coil. The authors present an Artificially Intelligent voltage controller to achieve the desired performance based on measurable variables in the device and supported by sensorless methods like temperature prediction with Artificial Intelligence (AI).