In electrified transportation systems, power system failures can lead to greater disasters. Therefore, the reliability of converters in transportation systems has been a concern. Fault-tolerant techniques are widely applied to ensure that converters can continue to supply loads under fault conditions. Fault diagnosis as a prerequisite for fault tolerance has also become a research hotspot. This paper proposes a fast method for fault diagnosis of high-frequency LLC converters. The proposed fault diagnosis method is based on the observation of the voltage across the resonant capacitor to determine and locate the faulty power switch, providing a basis for fault tolerance. This diagnosis method requires a voltage sensor, which is also necessary for some control methods. When applying these control methods, the proposed fault diagnosis method can be used without additional sensors, beneficial for cost reduction. A full-bridge LLC converter controlled by a digital signal processor was used as an experimental platform to verify the effectiveness and speed of the proposed diagnostic method. The results show that the proposed fault diagnosis method can achieve the fast diagnosis of high-frequency LLC converters in a short time and with only minimal computational resources.

To obtain more accurate contact mechanics model of joint interfaces theoretically, A theoretical contact mechanics model of joint interfaces based on fractal theory was proposed. An improved 3D WM fractal function was used to characterize the contact surface, contact load and contact area equations of asperities in elastoplastic deformation regime were established, solutions for the relationships of area-displacement and force-displacement in the elastoplastic deformation regime was done based on Hertz contact theory and fractal theory, and the present model was proven to be effective by comparing the present model to other four classical contact models and test data. Furthermore, simulations and numerical calculation results reveal nonlinear relation between the influence factors and the contact area.

The adaptive Kriging method is widely used in the engineering design for complex black-box problems, yet its accuracy is limited by imbalanced exploitation–exploration. This paper proposes a KNN-based maximization of the weighted expected prediction error (KMWEPE) method to address this challenge. For each iteration, the most sensitive region is identified by the leave-one-out cross-validation error (LOOCVE) and the distance between sample points. Two different sets of candidate points are generated, respectively, in the most sensitive region and the design space, in order to dynamically balance the local exploitation and global exploration. Then, the bias–variance decomposition method is used to convert the expected prediction error of each candidate point into the sum of the bias and the Kriging prediction variance. And the bias is replaced by the weighted sum of the LOOCVE of the K-nearest neighbors sample points based on KNN. Furthermore, the arithmetic sum of the bias and the Kriging prediction variance above is used to construct a new function. Finally, the candidate with the maximum weighted expected prediction error is selected as the new sample point for the next iteration. Six benchmark test functions, two publicly available datasets, and two engineering examples are tested to demonstrate the effectiveness of the proposed KMWEPE method in improving the model accuracy. The test results show that compared to the LHD and MEPE methods, the RMSE mean and standard deviation of the KMWEPE method decreased by an average of 31.6% and 28.8%, respectively.

Complex networks have become a center of interdisciplinary research. The robustness and reliability of complex systems can be determined by analyzing the characteristics of networks. The computer numerical control machine tool (CNCMT) is a typical complex system, and reliability is the key factor that affects the processing quality and performance of the CNCMT. The process of quantitatively determining the key functional components of CNCMT and carrying out reliability growth experiments on them remains difficult. A new importance-ranking method for the key functional components of the CNCMT is proposed in this study. First, the real characteristics of the CNCMT are analyzed, and the complex network of the CNCMT is constructed. Then, we study various indicators of the network and determine that the network has small-world characteristics. Finally, a variety of complex network characteristic indicators are fused, the weight of each indicator is determined based on the analytic hierarchy process (AHP), the importance of each node is quantitatively determined, and the fused indicators are compared with a single indicator.

To solve the problem of the poor dynamic performance of the machine due to low stiffness of joint interfaces, a theoretical normal dynamic stiffness model of joint interfaces based on fractal theory was proposed. An improved three-dimensional WM fractal function was used to characterize the contact surface, the vibration differential equation of the sphere-plane dynamic contact was established, the linearized stiffness of asperities in elastic deformation and elastoplastic deformation regime are derived respectively, and the normal dynamic stiffness model of the joint interfaces was established. At the same time, the relationship between the dynamic stiffness and the static stiffness of the joint interfaces was established. The analysis results show that, the dynamic stiffness of the joint was approximately equal to the static stiffness when the excitation frequency was much smaller than the natural frequency, and the dynamic stiffness was much larger than the static stiffness when the excitation frequency is much larger than the natural frequency, there also existed a resonant frequency that minimizes the dynamic stiffness.