Misalignment Faults Detection in an Induction Motor Based on Multi-scale Entropy and Artificial Neural Network
| 作者： | Alok Kumar Verma, Somnath Sarangi, Mahesh Kolekar |
| 作者单位： |
1Department of Electrical Engineering , Indian Institute of Technology , Patna , India 2Department of Mechanical Engineering , Indian Institute of Technology , Patna , India |
| 刊名： | Electric Power Components and Systems, 2016, Vol.44 (8), pp.916-927 |
| 来源数据库： | Taylor & Francis Journal |
| DOI： | 10.1080/15325008.2016.1139015 |
| 关键词： | misalignment; induction motor; multi-scale entropy; neural network; frequency spectrum; fault diagnosis; condition monitoring; |
| 原始语种摘要： | Abstract(#br)Misalignment is one of the most frequent faults observed in rotating machinery. In the present work, the misalignment fault of a motor shaft is studied using multi-scale entropy in combination with a back-propagation neural network algorithm. Experiments were performed, first with an aligned motor shaft, and then with a motor shaft that had angular and parallel misalignment at different operating speeds. Real-time motor current and vibration signals from aligned and different misaligned motor shafts were acquired for the diagnosis of faults. The existing literature mostly focused in the context of frequency-domain analysis. However, in this work multi-scale entropy is used, which accounts for the system complexity. A clear indication of reduction in complexity is observed... |
