Permanent Magnet Synchronous Machines (PMSMs) have been preferred for high-performance applications due to their high torque density, high power density, and potential for precise control and high efficiency over a wide operating range. During operation, monitoring the PMSM's health condition is crucial for detecting any anomalies so that performance degradation, maintenance/downtime costs, and safety hazards can be avoided. In particular, demagnetization of PMSMs can lead to not only degraded performance but also high maintenance cost as they are the most expensive components in a PMSM. In this paper, a simple equivalent two-phase model for surface-mount permanent magnet (SMPM) machines under permanent magnet demagnetization is formulated and a parameter estimator is proposed for condition monitoring purposes. The performance of the proposed estimator is investigated through analysis and simulation under different conditions, and compared with a parameter estimator based on the standard SMPM machine model. While both parameter estimators can detect the fault, the proposed estimator exhibits advantages over the standard-model-based estimator in terms of more significant information that can be extracted for fault diagnosis and condition monitoring.


Modeling and Parameter Identification for Condition Monitoring of Surface-Mount Permanent Magnet Machines Under Magnet Demagnetization

Division / Sponsors: 
Dynamic Systems & Control (DSCD)

Paper Proceedings: https://asmedigitalcollection.asme.org/DSCC/DSCC2020/volume/84270

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