Embedded neural network to model-based Permanent Magnet Synchronous Motor diagnostics

Effective fault detection and diagnosis (FDD) is especially important for some special applications, such as Navy ships operating in hostile environments. So far, FDD for nonlinear systems has not been fully explored. This paper makes an effort to fill the gap by extending an existing monitor architecture and a series of algorithms for FDD of permanent magnet synchronous motors (PMSM). A fault model is proposed for the stator winding turn-to-turn fault of PMSM. The model provides a good compromise between computational complexity and model accuracy and is versatile for both the healthy and the fault condition. Simulation studies demonstrate a good correspondence with both the theoretical analysis and the experimental observations in the paper.