Motor Winding Insulation Diagnosis and Prognosis Using Resistance Simulation Method


Reference #: 01489

The University of South Carolina is offering licensing opportunities for Motor winding insulation diagnosis and prognosis using resistance simulation method


The purpose of building Permanent Magnet Synchronous Motor (PMSM) model is to enable the winding insulation fault injection in motor, along with fault (clogging) in filter, to establish a multi-dimensional environment that can be used in automated contingency management development. This enables the maturity of the dynamic simulation-based ACM system for life support systems and integration of diagnosis, prognosis, and optimization into health and contingency management functions to mitigate the fault and reduce the risk of NASA system failure or mission failure.

Invention Description:

The invention establishes an equivalent resistance method to simulate the transient dynamics performance of winding insulation faults with high fidelity, which avoids experimental damage to motors and lays a solid foundation to accurate health management of motor driving systems.

Potential Applications:

Motors are critical components of industrial systems, electrical cars, and unmanned vehicles, etc. Hundreds of companies around the world are conducting related works on motor. For aerospace and civil aircraft, fault diagnosis and prognosis of motors will significantly reduce the operation and maintenance cost. Meanwhile, it will avoid catastrophic events that lead to system damage or human lives loss.

Advantages and Benefits:

The simulation results show that this novel equivalent resistance diagnosis and prognosis method can simulate the transient dynamic performance of the winding insulation fault with high fidelity, which is better than traditional resistance method. Compared with the program under Riemann sampling (covered by this innovation), the program under Lebesgue sampling framework can greatly improve the efficiency, simplify the process, and

improve the accuracy.

Patent Information:
For Information, Contact:
Technology Commercialization
University of South Carolina
Bin Zhang
Enhui Liu
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