| Title |
Real-Time Self-Complement System of Fault Diagnosis for Induction Motor Using Machine Learning and IoT Technique |
| Authors |
최동진(Dong-Jin Choi) ; 한지훈(Ji-Hoon Han) ; 홍선기(Sun-Ki Hong) |
| DOI |
http://doi.org/10.5370/KIEE.2019.68.5.662 |
| Keywords |
IoT ; Deep Learning ; Self-Complement ; Real-time ; Diagnosis |
| Abstract |
Existing fault diagnosis using deep Learning, has experimented by collecting data from a controlled environment. However, it is not easy to diagnose motor faults in various environments, becuase input data are measured with various disturbances together. For this reason, in this paper, the verification and learning process are separated and used in each system so that motor data of various environments can be considered. In the verification process, a data preprocessing process is added to verify and collect necessary data. A CNN-based in-depth learning algorithm is implemented and data is stored in real time. Since the data is re-learned based on the collected data, a model considering both existing features and newly input data is created. Even the continuous disturbance is also used as learning data, it is easier to cope with disturbance than the conventional method. As a result, a system applicable to an industrial field is proposed considering various environments. |