Support vector machine (SVM) is regarded as one of the most effective techniques for supervised learning, while the Gaussian kernel SVM is widely utilized due to its excellent performance capabilities. To ensure high performance of models, hyperparameters, i.e. kernel width and penalty factor must be determined appropriately. This paper studies the influence of hyperparameters on the Gaussian kernel SVM when such hyperparameters attain an extreme value (0 or ∞). In order to improve computing efficiency, a parameter optimization method based on the local density and accuracy of Leave-One-Out (LOO) method are proposed. Kernel width of each sample is determined based on the local density needed to ensure a higher separability in feature space while the penalty parameter is determined by an improved grid search using the LOO method. A comparison with grid method is conducted to verify validity of the proposed method. The classification accuracy of five real-life datasets from UCI database are 0.9733, 0.9933, 0.7270, 0.6101 and 0.8867, which is slightly superior to the grid method. The results also demonstrate that this proposed method is computationally cheaper by 1 order of magnitude when compared to the grid method.

Aircraft optimization design problems are mostly computationally intensive. These complicated problems probably contain mixed-variables, while most research has focused on continuous variables. This article sets up a mixed-variable surrogate-based optimization algorithm framework that includes a mixed-variable experiment design method and an inaccurate infilling method. The mixed-variable experiment design method combines improved successive local enumeration with enhanced stochastic evolution to deal directly with discrete variables. The inaccurate infilling method tends to find points with better fitness value and relatively low sample density so as to balance exploration and exploitation. Several numerical functions and a mixed-variable solid rocket motor performance matching design problem are solved using the modified surrogate-based optimization method. The results indicate that the proposed method is competitive compared with other heuristic algorithms and surrogate-based algorithms, and can deal with mixed-variable aircraft design problems effectively.

In the actual industrial scenario, motor fault classes gradually increase, and the fault data need to be acquired incrementally during the service. Continual learning (CL) has been introduced to the field of fault diagnosis (FD), aiming at constructing FD model with continuous evolution capability. However, the current research has the following limitations: 1) Assigning parameters for each task independently leads to linear increase in model complexity; 2) It relies heavily on the quality and completeness of historical data, and the research on incremental FD under few-shot conditions is not sufficient. To address the above limitations, this paper proposes a prototype learning framework for motor incremental fault diagnosis (PLFIFD) under few-shot conditions. First, a prototype classifier based on distance metric is proposed to avoid uncontrollable changes due to the update of parameters in the output layer. The prototype set is dynamically expanded instead of assigning network parameters individually. Second, a multi-objective loss function is designed to jointly optimize the classification boundary and prototype space distribution to enhance intra-class compactness and inter-class separability, and to improve the generalization ability under few-shot conditions and stability in dynamic scenarios. Finally, the effectiveness of PLFIFD is verified on induction motor (IM) and permanent magnet synchronous motor (PMSM).