Acquisition of physical kinetics of permanent magnet dc motor by reservoir computing

Abstract

This study discusses the use of reservoir computing in modeling dynamic systems of machinery for detecting anomalies. Precise modeling of a dynamic system of a machine is essential for detecting anomalies based on the comparison of predicted and actual outputs. To achieve this, motors were used as the subject of study, and three physical systems were constructed by progressively applying loads to their complex dynamic systems. By exploring the parameters of the reservoir and using preprocessed actual data, we successfully captured these physical systems through reservoir computing. Additionally, this study evaluated the predictive accuracy based on specific input patterns and assessed the model response to intentionally created abnormal conditions (manual stopping of the motor). In scenarios involving specific inputs that frequently occur in actual operational environments, the model showed significant discrepancies between predicted and actual values. These results indicate that reservoir computing can detect unexpected dynamic changes and effectively distinguish between normal and abnormal operating conditions. This study confirms that reservoir computing is an effective tool for the accurate modeling of machinery's dynamic systems and for detecting unexpected anomalies in real operational environments.