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The purpose of this paper is to present a new model to study inter‐turn short circuit faults in a permanent magnet synchronous machine.

The machine is modeled by using classical two‐axis theory, and the equations are modified to take into account the stator inter‐turn faults. A state space form of the system is presented for dynamic simulations.

The machine model is global and can work in both normal and fault conditions due to a fictitious resistance in the winding circuit. Various simulation results have been presented indicating the fault instant and its corresponding effect. Validation is carried out by transient time finite element simulations.

The model can serve as a step towards development of fault detection and diagnosis algorithms.

The purpose of this paper is to present a novel method to identify demagnetization faults in the magnet of a permanent magnet synchronous machine (PMSM) using some externally measurable parameter.

The machine is modelled by using permeance network theory. The new feature introduced in the permeance network is the subdivision of magnets into segments, modelled as bidirectional elements. These bidirectional elements allow taking into account the effect of one element on the other. To detect the demagnetization faults, a gradient‐based algorithm is also developed. This algorithm uses the permeance network model of the PMSM and measurement data of some parameter to find the distribution of remanent induction in the magnet segments.

The methodology presented is able to detect the demagnetization fault using an external data. The measurement data in this paper is obtained through finite element simulations. The fast and accurate convergence of the algorithm makes the model to find its place in magnet fault diagnosis. Results for different magnet fault types have been presented.

This new approach to detect demagnetization fault can serve as a step towards development of better fault‐detection algorithms and fault‐tolerant control schemes.