6533b826fe1ef96bd1284fb9
RESEARCH PRODUCT
Autoencoders and Data Fusion Based Hybrid Health Indicator for Detecting Bearing and Stator Winding Faults in Electric Motors
Jagath Sri Lal SenanayakaKjell G. RobbersmyrHuynh Van Khangsubject
Electric motorBearing (mechanical)Computer scienceStator020208 electrical & electronic engineeringFeature extractionCondition monitoringControl engineering02 engineering and technologySensor fusionlaw.inventionSupport vector machinelaw0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processinghuman activitiesFeature learningdescription
The main objective of a condition monitoring programs is to track the health status of critical components of a machine. In this paper, a hybrid health indicator is proposed to monitor the health status of bearings and stator winding of a motor. The proposed method is based on a feature learning from deep autoencoders and data fusion. The features can be learned by autoencoders using individual current and vibration signals, and then learning features are fused to make final health indicators. The experimental data from a permanent magnet synchronous motor is used to validate the proposed method. Promising results in detecting faults and severities of the stator and bearing faults at different load conditions have been obtained.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-10-01 | 2018 21st International Conference on Electrical Machines and Systems (ICEMS) |