A Comparative Study of Current, Vibration and Stray Magnetic Flux Based Detection for Parallel Misalignment Fault in Induction Motors

The misalignment fault is commonly caused by incorrect shaft positions between motor and load in electrical machines. It affects the mechanical symmetry of machine and thus causes mechanical oscillation on the shaft. In this paper, the parallel misalignment fault is analyzed based on stator current, vibration and stray flux in induction motors (IMs). The three-axis vibration sensor and an integrated flux sensor are used in order to stream vibration and stray flux for diagnostics process, respectively. The comparative results between stator current, vibration and stray flux are presented. Experimental results show that stator current and vibration-based analyses provide highly reliable results than stray flux for parallel misalignment fault. It is also shown that the proposed signatures in current and vibration vary very little with respect to load and motor drive type. Moreover, Multilayer Perceptron (MLP) based machine learning algorithm using vibration and stator current is carried out and it has excellent performance in the automatic detection of parallel misalignment fault.