Misalignment Faults Detection in an Induction Motor Based on Multi-scale Entropy and Artificial Neural Network
作者: Alok Kumar VermaSomnath SarangiMahesh 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: misalignmentinduction motormulti-scale entropyneural networkfrequency spectrumfault diagnosiscondition 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...
全文获取路径: Taylor & Francis  (合作)
影响因子:0.62 (2012)