Abstract: The safe operation of mechanical equipment is a basic requirement in the industrial manufacturing process. As an important component of mechanical equipment, rolling bearings are prone to unforeseen failures due to long-term operation under complex conditions. The occurrence of failures, no matter how big or small, will cause economic losses. Therefore, reliable diagnosis of rolling bearings is crucial. Aiming at the problem of insufficient feature recognition of convolutional neural networks under strong noise background, a deep feature extraction network integrating multiscale convolutional neural network (MSCNN) and bidirectional gated recurrent unit (BiGRU) is proposed. MSCNN and BiGRU are used to extract multiscale features and temporal features from noisy vibration signals respectively, and different weights are assigned to the fused features through the attention mechanism module to achieve important feature selection. Furthermore, in order to solve the problem of different feature distributions between the source domain and the target domain under variable working conditions, transfer learning is introduced in the proposed deep feature extraction network. The difference in feature distribution between the source domain and the target domain is measured by a multi-level distance formula, and the measurement result is added to the loss function. The back propagation of the loss is used to achieve the alignment of feature distribution between the source domain and the target domain. Finally, the model uses the SoftMax function as a classifier for rolling bearing fault diagnosis. Experimental comparison and analysis show that the proposed model has good migration ability and achieves a higher fault identification accuracy. Read More

Abstract: This study is focused on exploring the optimization of performance and sustainable development of hydrogen-based new energy vehicle systems. By taking into account critical factors such as hydrogen production, storage, fuel cell systems, and overall vehicle power systems, the objective is to enhance energy efficiency and mitigate environmental impact. The advancement of hydrogen vehicle technology is pivotal in achieving these goals. Hydrogen, recognized as a clean and plentiful energy source, holds potential for reducing greenhouse gas emissions and lessening reliance on fossil fuels within the transportation sector. The effective integration of efficient hydrogen production methods, dependable storage technologies, and cutting-edge fuel cell systems is essential for fully realizing the benefits of hydrogen-powered vehicles. These vehicles operate with zero emissions and can significantly contribute to addressing challenges related to climate change and urban air quality. Through this research, our aim is to contribute to ongoing efforts in developing sustainable transportation solutions. By optimizing the entire hydrogen energy chain—from production through to vehicle operation—we seek to maximize the advantages of hydrogen technology while minimizing its environmental footprint. This interdisciplinary approach merges engineering, environmental science, and policy analysis to pave the way toward a cleaner and more sustainable future for automotive transportation. Read More

Abstract: The strength of liquefied petroleum gas tank is related to production safety. In this paper, aiming at the structural strength of a 50m³ liquefied petroleum gas tank, ANSYS software is used for modeling and strength solving, and the strength of the spherical tank pillar is calculated. The results show that the strength of the liquefied petroleum gas tank meets the design strength standard and is safe. Read More

Abstract: Bearing fault diagnosis with limited samples is a key challenge in the field of intelligent manufacturing, necessitating the development of models capable of accurate learning from constrained data with strong generalization capabilities. This study proposes a novel framework combining autoencoders and generative adversarial networks, termed the Conditional Latent Space Generative Adversarial Network (CLS GAN), which utilizes autoencoders to learn the latent data distribution of signals, effectively capturing and reproducing the complexity of fault signals. Enhanced with an improved Transformer structure, this model is able to process and recognize long temporal features between signal segments, thereby boosting the accuracy and efficiency of fault diagnosis. Through the architecture of a Conditional GAN, a multi-class task discriminator is implemented, enabling effective fault type discrimination under conditions of limited samples. In situations where samples are restricted, the proposed CLS GAN model achieved an accuracy of 75% on the CWRU dataset, demonstrating the efficacy and practicality of an integrated framework that combines advanced generative adversarial networks and Transformer technology in mechanical fault diagnosis. Read More

Abstract: A compact dual-band magnetoelectric (ME) dipole antenna is proposed in this article. The antenna consists of two rectangular patches, two copper columns, and two substrates. The feeding is achieved through a bottom microstrip and rectangular slots, with the two symmetrical rectangular patches serving as electric dipoles and the two copper columns acting as magnetic dipoles. Electromagnetic simulations were conducted using Ansys HFSS, and the results indicate that the antenna achieves a return loss of less than -10 dB in the frequency ranges of 18.6-22.2 GHz and 27.7-32.1 GHz. The impedance bandwidths for the two bands are 17.8% and 14.8%, respectively. Two resonant points are observed at 19.1 GHz and 20.3 GHz in the low band, and at 28.8 GHz and 31.3 GHz in the high band. The realized gains are 4.3-5.2 dBi and 8.2-8.8 dBi respectively. Read More

Abstract: Laser cladding technology, renowned for its superior surface modification capabilities, is extensively utilized across aerospace, automotive, energy, chemical, metal products, and other heavy industrial sectors. However, issues such as porosity, cracks, and the non-uniformity of the cladding layer's composition and microstructure significantly hinder the advancement of this technology. Therefore, improving and enhancing the quality of the cladding layer is of paramount research importance. In recent years, there has been a growing body of research on the use of external physical fields to assist laser cladding. This research has expanded beyond the application of single physical fields to include the integration of electromagnetic fields, electromagnetic ultrasonic fields, and other coupled physical fields, which have effectively addressed the current challenges faced by laser cladding technology. This paper provides a comprehensive review of the progress made in the field of electromagnetic field-assisted laser cladding, based on an introduction to the principles and mechanisms of electromagnetic field assistance. Read More

Abstract: This paper aims to study the aging mechanism of power semiconductor devices in high humidity environments, propose anti-humidity optimization design methods, and explore the humidity reliability of these devices in-depth. The reliability of the devices can be examined through High Humidity High Temperature Reverse Bias Testing to simulate aging in high humidity environments and evaluate their performance. By analyzing the performance of power semiconductor devices under humidity stress, this research reveals the humidity aging mechanism and failure points, providing a theoretical basis for optimizing the design to enhance humidity resistance and lifespan [2]. Research on humidity reliability has gained frequent attention in recent years, but the causes and mechanisms of failure in chips are still unclear. Therefore, similar tests are required to analyze and summarize the failure parts. Simulations can be used to model the working conditions of devices in high humidity environments, simulating the diffusion of water vapor into chips, identifying failure patterns, analyzing moisture diffusion mechanisms, and improving the weak points of failure through comparative analysis using different materials. Finally, the most suitable anti-humidity materials can be identified for device improvement. Read More