Misalignment Faults Detection in an Induction Motor Based on Multi-scale Entropy and Artificial Neural Network

Abstract

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 with the faulty system. The proposed methodology is the first of its kind to detect the misalignment fault using the analysis of both vibration and current signals. The proposed work has also successfully identified for different types of misalignment.