To alleviate data distribution under different operating conditions, domain generalization (DG) has been applied in mechanical diagnosis. Still, its effectiveness is limited when unknown fault states appear in the target domain. Consequently, open set DG (OSDG) has emerged to identify unknown classes in unknown domains. However, data collection costs and safety concerns have resulted in a significant class imbalance in OSDG. This imbalance causes the decision boundary to be skewed toward abundant positive classes, ultimately leading to misclassifying unknown states and increasing security risks. Currently, there is a lack of methods to simultaneously address domain shift and class shift in an imbalanced unknown domain. To tackle this issue, this article proposes a multisource domain-class gradient coordination meta-learning (MDGCML) framework, which can learn the generalized boundaries of all tasks by coordinating gradients between interdomains and interclasses. Based on the MDGCML, a joint learning paradigm involving the sharing of parameters between open-set classifiers and closed-set classifiers is constructed to enable quick adaption of the model to unknown domains. The superior performance of the proposed framework has been verified on two datasets.

Railway track systems serve as vital parts of urban mobility and intelligent transportation. Detecting defects in rail track systems not only avoids unexpected downtime but also safeguards passenger lives. Moreover, defect classification holds great economic value, which optimizes both traffic operations and management strategies. Compared to lab tests and track recording vehicle-based field tests, defect detection and classification using in-service trains is an emerging area of study. This enables continuous monitoring, increases carrying capacity, and reduces maintenance costs, but it also requires robust performance and compatibility with various weather conditions. Considering the precipitation characteristics in tropical regions, this paper proposes a novel online defect detection and classification method for mass rapid transit (MRT) railway track systems by integrating a non-contact train-borne monitoring system (TBMS) and an adaptive density-based spatial clustering of applications with noise (Adaptive-DBSCAN) algorithm. The TBMS is developed based on the inductive coupling theory, affirming real-time, contactless, and effective defect detection in tropical regions with high annual and intense short-duration rainfall. By assessing the voltage health ratio (VHR) of the train-rail electrical path, the TBMS can simultaneously monitor defects from rail, ballast, and sleepers/ fasteners. To classify the group of each defect for maintenance decisions, Adaptive-DBSCAN is applied using VHR as inputs and calibrates the algorithm parameters adaptively. Therefore, it avoids the exhaustive traversal typically needed for parameter selection in DBSCAN while preserving accuracy. Experiments conducted on an in-service MRT train (operating at 80 km/h) verified the effectiveness of the proposed method.

The common-mode (CM) choke is critical integral part of an electromagnetic interference (EMI) filter. An accurate electrical model of the CM choke is crucial to simulate and evaluate the EMI filter performance with confidence. Numerical simulation has emerged as a feasible solution to realize the above-mentioned objectives without the presence of physical products, and thus reducing the trial-and-error process and achieving the choke design speedups. This paper proposes a comprehensive process for the characterization and modeling of single-phase CM chokes based on finite-element analysis (FEA). By extracting the transmission parameters through 3-D model of choke, both impedance magnitudes and phases of its CM or differential-mode (DM) can be collected. These impedances will then be used to derive the behavioral model. The results are validated experimentally with good agreement up to 100 MHz.

Single-phase common-mode chokes (CMCs) are key components in electromagnetic interference (EMI) filters to mitigate conducted emissions caused by the switching power converters. Finite-element analysis (FEA) has been adopted as one of the simulation tools to model a CMC for the extraction of its impedance frequency response. Most literatures focus on the extraction of impedance magnitude, but few explore the analysis of the impedance phase information. This article investigates the phase sensitivity of FEA modelled single-phase CMCs, which reveals the impact of various design parameters on simulation results at frequencies up to 100 MHz.

The state-of-charge (SoC) stands as a pivotal measure for ascertaining a battery's remaining capacity. Accurate SoC estimations can meaningfully enhance a battery's operational longevity, fortify safety standards, and enrich user experience. This paper presents an improved open circuit voltage (OCV)-based partnership for a new generation of vehicle (PNGV) model, specifically tailored for estimating the SoC of LiFePO4 blade batteries. These batteries are distinctively characterized by their advantages in safety, energy density, and thermal management. The proposed model uniquely integrates the SoC-dependent property of the battery's internal resistance, facilitating a marked improvement in estimation accuracy over existing PNGV models. Experimental results underscore the capability and effectiveness of the proposed model in estimating real-time discharging curves, achieving a remarkably low relative error rate of 0.85%.

A novel coil-based electromagnetic-induced thermo-acoustic system is presented for detecting the flaws inside a rail. The fundamental is derived and the overall energy density distribution is simulated using finite element method. This paper gives an overview of the system architecture and describes the design process in detail. A mixed numerical experimental methodology is employed to extract the lumped parameters of a planar coil with the ferrite plate for designing the matching network, and then the coil and rail are co-simulated to observe the current density distributions and directions. Through the relationship of energy density and depth in the rail, it is found that the thermal energy mainly concentrates at the surface local area. From the interaction between the coil and rail, the inductive power transfer topology is illustrated and the simplified equivalent circuit model is further obtained. By analyzing the simulated and measured data, the changes in the resistance and inductance are shown with the frequency increasing. The induced ultrasonic wave propagation is simulated inside the rail with flaws, where the wavefronts and reflected signals are observed. Finally, the experimental results demonstrate that the proposed design is feasible and a crack with a diameter of 8 mm can be detected in the rail.

The loop inductances of a silicon carbide (SiC) half-bridge power module (HBPM) have a direct impact on its performance. This paper describes an improved two-port network method to measure and extract the loop inductances of a SiC HBPM with good accuracy.