## 5 Conclusions  
This paper proposes an automatic and accurate diagnosis method for demagnetization faults in PMSMs using three PNNs. The simulation and experimental results justify the correctness and effectiveness of the proposed method. (1) Arranging three additional toroidal-yoke coils at the bottom of the three stator slots in DDPMSMs can capture many demagnetization faults, and not only contain early fault information, but also contain demagnetization fault location information. Compared with the existing search coil-based method, which installs a search coil on each tooth of the machine to locate the demagnetization faults, the number of search coils in this method is greatly reduced; therefore, it hardly increases the volume and cost of the machine, and does not disturb the design parameters and performance of the machine. Furthermore, the distance between adjacent toroidal-yoke coils is approximately polar distance. Thus, the design of the search coil does not need the structure parameters of the machine; it is easy to generalize this method to other PMSMs. In addition, the signal of toroidal-yoke coils is largely unaffected by magnetic circuit saturation, and therefore, the detection precision of the method is high. (2) For PMSMs with very many magnetic poles, there are many modes of demagnetization faults, which makes it difficult to determine the number of demagnetized permanent  
Figure 16: The experimental results of residual voltage of under fault3. (**a**) The residual voltage of TYC1, TYC2, and TYC3. (**b**) The residual voltage waveform of \(S_{\text{m}}\) and \(S_{\text{a}1}\). magnets. Therefore, this paper proposes a rotor partition method to solve this problem. The rotor permanent magnet is divided into zones according to each pair of permanent magnets, and the healthy condition of each pair of permanent magnets is diagnosed in turn, and then the demagnetized permanent magnets are located. (3) Three demagnetization fault identified signals are constructed, which can indicate the location of demagnetized permanent magnets. Theoretical analysis and simulation results prove the robustness of the constructed signal to demagnetization degree, load, and speed. (4) The three PNN-based diagnosis model is developed to determine demagnetization fault modes step by step. The constructed demagnetization fault identified signals are directly input into PNNs, and then the PNNs extract fault features and classify fault modes. Compared with the existing fault diagnosis methods based on deep learning, the proposed method realizes demagnetization fault mode classification with a lightweight model, and has the advantages of a simple network learning process and quick training. The accuracy and robustness of the developed diagnostics system using three PNNs were confirmed by testing unseen modes (captured experimentally and by FEM simulation). The overall accuracy of diagnosis is 99.2%. Conceptualization, Y.X. and K.W. ; methodology, Y.X. ; software, Y.X. ; validation, J.Z. and Y.X. ; formal analysis, S.Y. and K.W. ; investigation, Y.X. and H.Z. ; resources, S.Y. ; data curation, S.Y. ; writing--original draft preparation, Y.X. and K.W. ; writing--review and editing, Y.X. and H.Z. ; visualization, K.W. ; supervision, Y.X. and H.Z. ; project administration, Y.X. ; funding acquisition, K.W. and J.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by a project supported by the National Natural Science Foundation of China: 51907201 and funding from the Military Logistics Research Program: BHJ22R047. Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The authors declare no conflict of interest. The first database for training and testing the first PNN. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \# & \begin{tabular}{c} **Speed** \\ **(rpm)** \\ \end{tabular}  & \ \begin{tabular}{c} **Current** \\ **(Arms)** \\ \end{tabular}