A guidance for preparing permanent magnet synchronous machines (PMSMs) that exhibit huge potential for applications in transportation electrification owing to their high power density and efficiency is provided. However, concerns remain regarding the use of conventional PMSMs for safety-critical aircraft systems. The failure modes of PMSMs in relation to their electrical, magnetic, and mechanical properties are reviewed. Failure tree analysis is used to list a series of independent faults. The corresponding fault diagnosis methods including model, signal processing, and artificial intelligence (AI)-based models are demonstrated separately. Moreover, state-of-the-art designs for fault-tolerant PMSMs are presented and suggested. Advanced control methods for machine failure scenarios are also discussed, which could potentially pave the way for post-fault aerospace operations. A case study of a fault-tolerant PMSM design is introduced, along with some post-fault operation mechanisms. All these efforts could improve the reliability and safety of PMSM for future aircraft applications.

Motor drives form an essential part of the electric compressors, pumps, braking and actuation systems in the More-Electric Aircraft (MEA). In this paper, the application of Machine Learning (ML) in motor-drive design and optimization process is investigated. The general idea of using ML is to train surrogate models for the optimization. This training process is based on sample data collected from detailed simulation or experiment of motor drives. However, the Surrogate Role (SR) of ML may vary for different applications. This paper first introduces the principles of ML and then proposes two SRs (direct mapping approach and correction approach) of the ML in a motor-drive optimization process. Two different cases are given for the method comparison and validation of ML SRs. The first case is using the sample data from experiments to train the ML surrogate models. For the second case, the joint-simulation data is utilized for a multi-objective motor-drive optimization problem. It is found that both surrogate roles of ML can provide a good mapping model for the cases and in the second case, three feasible design schemes of ML are proposed and validated for the two SRs. Regarding the time consumption in optimizaiton, the proposed ML models can give one motor-drive design point up to 0.044 s while it takes more than 1.5 mins for the used simulation-based models.

This study uses the Neural Network (NN) technique to optimize design of surface-mounted Permanent Magnet Synchronous Motors (PMSMs) for More-Electric Aircraft (MEA) applications. The key role of NN is to provide dedicated correction factors for the analytical PMSM mass and loss estimation within the entire design space. Based on that, a globally optimal design can be quickly obtained. Matching the analytical estimation with Finite-Element Analysis (FEA) is the main research target of training the NN. Conventional analytical formulae serve as the basis of this study, but they are prone to loss accuracy (especially for a large design space) due to their assumptions and simplifications. With the help of the trained NNs, the analytical motor model can give an estimation as accurate as the FEA but with super less time during the optimization process. The Average Correction Factor (ACF) approach is regarded as the comparison method to demonstrate the excellent performance of the proposed NN model. Furthermore, a NN aided three-stage-seven-step optimization methodology is proposed. Finally, a Pole-10-Slot-12 PMSM case study is given to demonstrate the feasibility and gain of the NN aided multi-objective optimization approach. In this case, the NN aided analytical model can generate one motor design in 0.04 s while it takes more than 1 min for the used FEA model.