In this paper, solving the problem of the noise covariance matrix parameters tuning of the extended Kalman filter (EKF) and unscented Kalman filter algorithms (UKF) is difficult. A speed estimation strategy for a permanent magnet synchronous motor (PMSM) based on particle swarm optimization (PSO) optimized Kalman filter (KF) series algorithms is proposed. By using MATLAB/Simulink, in this paper, 20 effective simulation experiments on the noise covariance matrix parameter optimization process are conducted to obtain the optimal covariance matrix parameters of the extended Kalman filter and unscented Kalman filter. Moreover, EKF, PSO-EKF, UKF, and PSO-UKF are also compared to verify the effectiveness of the particle swarm optimization algorithm in optimizing the systems using the extended Kalman filter and unscented Kalman filter. For the error of speed estimation, taking 4000 rpm as a reference, the system using PSO-EKF has improved by 2.125% compared to that using EKF, and the system applying PSO-UKF has improved by 0.55% compared to the system applying UKF. For the error of electrical angle estimation, taking the system errors of original algorithms as references, the system adopting PSO-EKF has decreased by 60% compared to that adopting EKF, and the system using PSO-UKF has decreased by 47% compared to the system using UKF.

As an important part of electronic equipment, the reliability of analog circuits directly affects the safe and stable operation of electronic equipment. The optimal measurement node set selection and intelligent fault diagnosis are combined in this paper, and an analog circuit fault diagnosis method incorporates a multi-objective selection of measurement nodes. Firstly, the fault differentiation is calculated by a classification algorithm to construct the fault information table of measurement nodes, and then the number of measurement nodes and the number of faults are set as the two objective functions of the multiple objective beluga whale optimization (MOBWO) algorithm to realize the optimal measurement node set selection. Secondly, the fault data set from multiple measurement nodes is inputted into the fault diagnosis model based on the adaptive particle swarm optimization algorithm and stacked denoising autoencoder (APSO-SDAE), in which the hyperparameter combination of the SDAE model is optimized by the APSO algorithm based on the fitness value in order to improve fault classification capability, and, finally, the analog circuit fault diagnosis task is completed. In order to verify the effectiveness and superiority of the above method, two standard circuits are selected for experimentation, and diagnostic accuracies of 97.87% and 98.41% are obtained. The results show that the method proposed in this paper can effectively improve the fault diagnosis accuracy, which is better than other comparative methods.