The increase in the reliability and safety levels of electrical power devices plays a key role in the transition toward aviation electrification. Safety-critical equipment, such as propulsion motors, must comply with stringent requirements to obtain airworthiness certification. Permanent Magnet Synchronous Motors (PMSMs) have emerged as a suitable technological solution for high-performance flight control actuation systems, and their extension to propulsive functions appears promising. Nevertheless, the reliability of PMSM power electronics converters is still far from the levels required by certification; therefore, motors must incorporate fault-tolerant capabilities in order to be considered flightworthy. In this context, the development of prompt and effective Fault Detection and Isolation (FDI) algorithms is essential. This paper presents the Model-in-the-Loop (MIL) validation of a Neural Network (NN) algorithm for the detection and isolation of MOSFET faults and open-phase faults in PMSM power drives. The approach is based on a feedforward NN trained on simulation-generated data for a multi-class classification problem, combined with a counter-based decision logic to ensure fast, robust, and computationally efficient operation. A feature selection analysis is also carried out to reduce the network size without significant degradation in the classification accuracy. For the MIL validation, the NN-based algorithm is integrated within a high-fidelity dynamic model of an electrically driven UAV propeller, including the simulation of the three-phase PMSM with Field-Oriented Control (FOC) of the motor currents, digital signal processing, together with a model of the aerodynamic loads derived from Blade Element Momentum Theory (BEMT). Faults are injected at different operating speeds and under uncertain environmental conditions, to evaluate the FDI performance in terms of detection latency, isolation capability, and robustness. The results demonstrate the effectiveness of the approach, which, owing to its straightforward generalization, can be extended to other fault categories and leveraged to support the development of PMSMs with fault-tolerant control capabilities.

The increase in reliability and safety levels of electrical power devices is key to the transition toward aviation electrification. Safety-critical equipment, such as propulsion motors, must comply with stringent requirements to obtain airworthiness certification. Permanent Magnet Synchronous Motors (PMSMs) have emerged as a suitable solution for high-performance flight control actuation, with promising extension to propulsive functions. Nevertheless, the reliability of PMSM power electronics converters remains below certification requirements, so fault-tolerant capabilities are essential for flightworthiness, making prompt and effective Fault Detection and Isolation (FDI) algorithms essential. This paper presents the Model-in-the-Loop (MIL) validation of a Neural Network (NN) algorithm for the detection and isolation of MOSFET faults and open-phase faults in PMSM power drives. The approach combines a feedforward NN, trained on simulation-generated data for a multi-class classification problem, with a counter-based decision logic ensuring fast and robust isolation, using a compact architecture. A feature selection analysis reduces the network size at a modest accuracy cost. For MIL validation, the NN-based algorithm is integrated within a high-fidelity dynamic model of an electrically driven UAV propeller, comprising a three-phase PMSM with Field-Oriented Control (FOC), digital signal processing, and aerodynamic loads derived from Blade Element Momentum Theory (BEMT). Faults are injected at different operating speeds, during speed transients, and under combined parametric uncertainty to evaluate the FDI performance in terms of detection latency, isolation capability, and robustness. The results demonstrate the effectiveness of the approach, which can be extended to other fault categories and support the development of fault-tolerant PMSM control.