| Title |
Predictive Maintenance and Fault Diagnosis of Three-Phase Induction Motor Using MCSA(Motor Current Signature Analysis) |
| Authors |
Ki Dong Kim ; Young Il Kim |
| DOI |
https://doi.org/10.6110/KJACR.2021.33.12.656 |
| Keywords |
전동기전류 징후분석(MCSA); 예지 보전; 유도 전동기; 모델 기반의 고장진단; 상태 기준 정비(CBM) MCSA(Motor current signature analysis); Predictive maintenance; Induction motor; Model based fault diagnosis; CBM(Condition based maintenance) |
| Abstract |
Three-phase induction motors are widely used as driving parts of rotating machines in industrial fields because they are relatively inexpensive, easy to install, and easy to maintain. However, defects may occur due to various factors such as problems with the motor itself, structural problems with facilities, or problem with operation. Failure of the motor can have a huge impact on the productivity, quality, and safety in addition to the repair cost of the motor. Generally, vibration monitoring, lubrication analysis, temperature measurement, and infrared sensing are used to diagnose motor faults in the past. Recently, there has been a growing number of studies on the motor current signature analysis (MCSA) method, which has a wide range of fault diagnosis that enables real time monitoring of the motor status online. This paper is a study on fault diagnosis and predictive maintenance of motor using model-based MCSA in laboratory and fields. In conclusion, the fault of the motor can be accurately diagnosed with MCSA. In particular, by monitoring the trend value, it is possible to easily determine the degree of the defect. This work confirms that CBM-based predictive maintenance can be used for diagnosis of three-phase induction motors. |