| Title |
AI Based State Discriminator of Motor System for Smart EOCR |
| Authors |
Kyung-Min Lee ; Chul-Won Park |
| DOI |
http://doi.org/10.5207/JIEIE.2023.37.5.070 |
| Keywords |
AI; DNN; EOCR; MCC; Predictive maintenance technology; State discriminator |
| Abstract |
Recently, there has been growing interest in applying AI (artificial intelligence) technology to predict electric motor defect, perform prevention and maintenance, and reduce recovery costs and losses. This paper proposes an AI-based state discriminator for the electric motor system to improve the existing EOCR into a smart EOCR by adding the predictive maintenance function of the CLOUD environment. Firstly, the smart EOCR based motor system is introduced. Next, five state learning data sets collected from the motor system are constructed. After designing a state discriminator with DNN (Deep Neural Network), a widely used AI technique, and implementing it using the Python language. We prove the effectiveness of the state discriminator. |