Effectiveness of Neural Fault Detectors of Permanent Magnet Synchronous Motor Trained with Symptoms from Field-Circuit Modeling

Abstract

Permanent magnet synchronous motors (PMSMs) are increasingly used in industrial drive applications. However, these motors can also undergo various failures, causing production line downtime and resulting in economic loss. Modern diagnostic systems allow the analysis of technical conditions based on a dataset containing fault symptoms. In most cases, the development of diagnostic patterns indicates the necessity for interference in motor mechanical construction. This fact speaks to the use of mathematical models, particularly those based on finite-element methods. This study investigated the possibility of fault classification using self-organizing Kohonen maps and a multilayer perceptron, based on training with data from a field-circuit model. The objective of this study is to demonstrate that such neural systems can detect and classify real motor faults under different operating conditions. Research has focused on stator-winding faults, demagnetization, and mixed faults. Experimental tests demonstrated the impressive diagnostic capability of the shallow neural networks developed for diagnostic tasks.