Open AccessIntegration of intelligent diagnosis system and augmented reality for electric motors
Author(s) -
Yu Ching Lin,
Heng Chuan Kan,
Jian Lü,
Chih Min Yao,
Yu Chia Liao,
Chun Hui Chung,
Kai Chung Shih,
MingChe Tsai
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1113/1/012023
Subject(s) - automation , electric motor , computer science , fault (geology) , control engineering , induction motor , engineering , voltage , electrical engineering , mechanical engineering , seismology , geology
With the rapid development of industrial automation technology, electric motor control has also become an important technology. However, motor failure has many possible causes, and it is not easy to detect in advance. Therefore the fault diagnosis is an important issue for motors. The augmented reality (AR) technology is also developing towards various industrial and educational application in recent years. This research is focused on developing the intelligent diagnosis system and AR application for electric motors. The intelligent diagnosis system applies cloud data management and machine learning methods to predict the health status of electric motors. And the time-recurrent Long Short-Term Memory (LSTM) neural network algorithm is applied to establish a motor health diagnostic model. The experimental results show that the motor diagnosis method can predict the health status of motor effectively. The proposed system will also provide more industrial application services in the future.