The challenge of aerodynamic noise is a key obstacle in the advancement of low-pressure tube ultra-high-speed maglev transportation, demanding urgent resolution. This study utilizes a broadband noise source model to perform a quantitative analysis of the aerodynamic noise produced by ultra-high-speed maglev trains operating in low-pressure environments. Initially, an external flow field calculation model for the ultra-high-speed maglev train is presented. Subsequently, numerical simulations based on the broadband noise source model are used to examine the noise characteristics. The impact of the train speed and pressure level on noise generation is investigated accordingly. Subsequently, a correlation formula is derived. The results reveal that the amplitude of sound source changes in the streamlined region of the head and tail cars of the train is large, and the amplitude of changes for the middle car is smaller. The noise source strength increases with speed, with a quadrupole noise source becoming dominant when the train speed exceeds 600 km/h. At a speed of 1000 km/h, the noise source intensity from the streamlined area at the rear of the train overcomes that at the front. Furthermore, the noise source decreases as the pressure level in the tube decreases. When the pressure level drops to 0.01 atm, the quadrupole noise source intensity of a train running at 600 km/h significantly weakens and falls below that of the dipole noise source.

The deployment of vehicle micro-motors has witnessed an expansion owing to the progression in electrification and intelligent technologies. However, some micro-motors may exhibit design deficiencies, component wear, assembly errors, and other imperfections that may arise during the design or manufacturing phases. Consequently, these micro-motors might generate anomalous noises during their operation, consequently exerting a substantial adverse influence on the overall comfort of drivers and passengers. Automobile micro-motors exhibit a diverse array of structural variations, consequently leading to the manifestation of a multitude of distinctive auditory irregularities. To address the identification of diverse forms of abnormal noise, this research presents a novel approach rooted in the utilization of vibro-acoustic fusion-convolutional neural network (VAF-CNN). This method entails the deployment of distinct network branches, each serving to capture disparate features from the multi-sensor data, all the while considering the auditory perception traits inherent in the human auditory system. The intermediary layer integrates the concept of adaptive weighting of multi-sensor features, thus affording a calibration mechanism for the features hailing from multiple sensors, thereby enabling a further refinement of features within the branch network. For optimal model efficacy, a feature fusion mechanism is implemented in the concluding layer. To substantiate the efficacy of the proposed approach, this paper initially employs an augmented data methodology inspired by modified SpecAugment, applied to the dataset of abnormal noise samples, encompassing scenarios both with and without in-vehicle interior noise. This serves to mitigate the issue of limited sample availability. Subsequent comparative evaluations are executed, contrasting the performance of the model founded upon single-sensor data against other feature fusion models reliant on multi-sensor data. The experimental results substantiate that the suggested methodology yields heightened recognition accuracy and greater resilience against interference. Moreover, it holds notable practical significance in the engineering domain, as it furnishes valuable support for the targeted management of noise emanating from vehicle micro-motors.