In recent years, deep learning-based intelligent fault diagnosis methods have made significant progress. However, most of these approaches rely heavily on large amounts of training data and involve substantial computational costs, which can adversely affect both model performance and real-time applicability. To achieve high accuracy with low computational overhead in endto-end fault diagnosis, this article directly utilizes 1-D vibration signals as model inputs and introduces frequency domain analysis into the Swin-Transformer framework, proposing a novel frequency-enhanced Transformer method. Specifically, we design a frequency-enhanced block that performs frequency domain transformation and data augmentation on 1-D vibration signals. In this process, the Fourier-transformed vectors are reorganized into a 2-D matrix, providing a richer and more informative input representation. Additionally, a new frequency-enhanced attention mechanism is developed to replace the traditional attention mechanism during model training. Building upon these frequency-domain data augmentation and attention enhancements, the network architecture is further optimized. Experimental results demonstrate that, compared with the standard Swin Transformer using time-domain signals, the proposed method reduces computational cost by approximately 9% while achieving 99.225% accuracy. Moreover, our approach exhibits faster convergence and superior generalization capabilities across cross-domain tasks. Finally, when applied to real-world industrial motor data, the method achieves 89.2% accuracy, highlighting its practical value for industrial fault diagnosis.

With the rapid development of modern industrial systems, production equipment has become increasingly complex, leading to high diversity in equipment types, fault modes, and operating conditions. Equipment failures not only degrade production efficiency but may also pose serious threats to operational safety, which places higher demands on the accuracy and reliability of fault diagnosis methods. To address these challenges, this paper proposes a multi-scale feature fusion fault diagnosis method based on a cross-attention mechanism. Built upon the Vision Transformer (ViT) framework, the proposed method employs two feature extraction branches operating at different scales to model and fuse multi-scale characteristics of vibration signals, thereby enabling more comprehensive feature representation. Specifically, the raw one-dimensional vibration signals are transformed into two-dimensional frequency-domain representations with an input size of 32×32, which are then fed into a dual-branch Vision Transformer architecture. Furthermore, a cross-attention-based feature interaction module is introduced to facilitate effective information exchange and complementary fusion between different scale branches, enhancing the discriminative capability of fault features. Experimental results demonstrate that the proposed method achieves a fault diagnosis accuracy of 98.942%, outperforming several state-of-the-art approaches, including CrossViT, ViT, ResNet, VGG, and WDCNN.

Electric motors are common devices widely used in the industrial sector, making the study of motor fault diagnosis highly representative. In particular, for rail grinding vehicles, which play a significant role in the preventive maintenance and periodic upkeep of railway tracks, ensuring optimal train operation is of paramount importance. However, due to harsh operating conditions, the grinding motors on rail grinding vehicles frequently experience failures. Typically, these motors are periodically inspected and repaired by railway workers, which often leads to delayed handling of faulty motors, thereby compromising the efficiency of rail grinding operations and increasing the maintenance costs associated with motor repairs. Consequently, there is a need to investigate a fault diagnosis model for grinding motors and establish a system for remote fault diagnosis of these motors. To address this issue, the first step involves analyzing the maintenance records of grinding motors to identify common failure locations and types, and subsequently collecting corresponding vibration data. Next, a fault diagnosis model is developed based on the specific failure characteristics of grinding motors. This model is trained and optimized using a data set of vibration data from grinding motors to determine a suitable fault diagnosis model for this specific application. Finally, the developed fault diagnosis model for grinding motors is applied to diagnose faults in these motors, thereby validating the practical effectiveness of the model. By conducting this research, it is anticipated that a comprehensive understanding of the fault diagnosis process for grinding motors can be achieved, leading to the implementation of a remote fault diagnosis system for these motors. Ultimately, this will contribute to improved operational efficiency and reduced maintenance costs in rail grinding operations.

Abstract The cooperative planning in intermodal transport networks can obtain the global optimal decision under the premise of ensuring the data privacy of each role in the cooperation and avoiding massive data transmission. For the control of container flow in intermodal transport networks, the distributed model predictive control (DMPC) method can effectively realize cooperative planning, but the convergence speed of the existing DMPC methods is slow. Therefore, this study attempts to construct faster DMPC methods for cooperative planning. The Jacobi proximal distributed model predictive control (JP‐DMPC) and dual consensus distributed model predictive control (DC‐DMPC) methods are constructed for container flow control based on two variants of alternating direction method of multipliers (ADMM). The simulation experiments prove that the convergence speed of JP‐DMPC and DC‐DMPC methods is higher than that of the state‐of‐the‐art method on the premise that the time cost and interaction data volume of each iteration do not change much, and the DC‐DMPC method improves planning speed particularly significantly. This study provides new methods for intermodal transport cooperative planning and has significance for the development of synchromodal transport.

In storage systems, there are heat differences between data. Traditional algorithms such as LRU are limited by specific data structures. These methods cannot be well applied to industrial big data storage systems. Methods based on "temperature" are usually limited by static parameters and it is unable to adapt to dynamic load. Based on Newton's law of cooling, we proposed an identification model called AdjustDTM, with adjustable parameters. Our method identifies the hot and cold by assigning the attribute "temperature" to the data. Then, the model can dynamically adjust the parameters according to accessing interval and frequency. Our model can also preheat the correlative data. Finally, The experimental results showed that the hit rate of AdjustDTM is higher than other strategies.

针对自动扶梯原有故障检测装置的老化以及缺乏远程监测功能的问题,设计了一套无线数据采集系统.利用ESP-WIFI-MESH网络所具备的低成本、广覆盖、可扩展等特点,可以在具备较低部署成本的情况下,以无线的方式在系统的各个节点之间实现数据传输.数据采集系统由一个中心节点和若干边缘节点构成,边缘节点实现数据采集功能,中心节点实现数据缓存及上传功能.该系统通过对扶梯运行数据的实时采集与上传,实现对于自动扶梯运行状态的监测,保障自动扶梯的安全运行.

在桥式起重机运行环境双目视觉三维建图任务中,为减少因反光耀斑所造成的建图误差,文中对双目相机采集的图像使用偏振滤镜对偏振光进行过滤,使用搭建的实验台进行了实验,比较了偏振滤镜对三维地图精度的影响.结果表明:偏振滤镜可以有效过滤物体表面反光,减小建图误差,提高建图结果的鲁棒性.

Bridge cranes must be able to sense their working environment to achieve autonomous operation. An active visual mapping system is presented in this research to adapt the measurement range based on the crane's operational state. The rotation angles of the two servos are computed based on the crane's speed so that the binocular camera is deflected and a greater field of view is gained in front, improving the crane's safety. An experimental platform is developed to simulate the operation process of the active vision 3D mapping system, and experiments are carried out using the approach proposed in this paper. The experimental results indicate that the technology can successfully enhance the mapping scope of the bridge cranes' digital operating environment and improve crane operation safety.

The corrosion of grounding materials seriously threatens the safe operation of the power system. The corrosion resistance of four typical grounding materials as carbon steel, galvanized steel, Zn–Al-coated steel, copper was studied in acid red soil. The results show that carbon steel, galvanized steel, Zn–Al-coated steel, copper exhibit different corrosion resistance behaviors, respectively. The corrosion rate of these grounding materials usually increases first and then slowly decreases. Pitting corrosion is the main corrosion feature of carbon steel. The corrosion rate of carbon steel is the largest compared to the other three grounding materials. The corrosion rate of galvanized steel is higher than that of copper. Copper has a low corrosion rate and exhibits good corrosion resistance, but the cost of copper is high and it causes heavy metal pollution. The corrosion rate of Zn–Al-coated steel is the lowest compared to the other three grounding materials, and it has the best corrosion resistance.

Aiming at the problem of obtaining internal parameters of binocular camera-lidar, a joint calibration method is proposed. Firstly, the corner points in the left and right images are detected and sub-pixel precision is performed, and the corner points in the left and right images are matched through epipolar constraints. Then, the internal parameters of the binocular camera are obtained according to the three-dimensional space coordinate relationship and the corner point matching relationship. Through the coordinate relationship between the feature points of the lidar and the feature points of the binocular camera, the binocular camera and the lidar are jointly calibrated to obtain the external parameters of the binocular camera-lidar system. Finally, an experiment was designed and carried out for the method proposed in this paper. The experimental results show that the calibration method proposed in this paper is effective.

铁路轨道打磨车打磨电动机工作环境复杂恶劣,故障发生率相对较高,并且打磨电动机作为铁路轨道打磨车的关键部件,其运行状态直接关系到轨道打磨的效率与质量,故可靠的故障诊断技术是提高打磨效率与打磨质量的关键技术之一.文中研究基于数据驱动的方式,通过传感器采集不同打磨电动机数据构建数据集,采用不同的机器学习算法构建电动机智能故障诊断模型,对打磨电动机运行状态进行诊断,并通过对比不同算法的诊断准确率,探索更加适用于打磨电动机的故障诊断算法.结果表明,所研究的智能故障诊断技术准确度较高,对提高打磨电动机运行可靠性以及提高其工业智能化程度具有较大意义.

分布式控制相比于集中式控制可靠性、灵活性高,系统易维护和扩展.为了实现起重机信息化、智能化,设计了一种基于以太网的桥式起重机智能化控制系统,阐述了分布式控制系统的工作原理并进行了验证.桥式起重机由变频器带动电机,驱动大车、小车和主起升运动,在保证运行安全的同时需要采集运行数据.根据设计需要,该控制系统由两个现场控制器、一个数据采集控制器和若干个控制单元组成,利用工业以太网进行通信.用OPNET软件对该分布式控制系统端到端时延进行仿真,仿真结果表明该控制系统的设计合理、高效,有效地提高了桥式起重机运行的可靠性和信息化程度.

为降低铁路隧道防护门门体虚掩、门体脱落等情形所带来的安全风险,设计了一套完整的远程监控系统.该系统包括数据采集、数据传输以及数据处理三个部分,首先,利用树莓派作为中心采集器采集数据,然后,通过WiFi或4G模块将数据以MQTT协议的格式发送至MQTT服务器端进行转发,最后,监控平台对收到的数据进行存储、分析与展示.该系统能较好地适应隧道防护门的应用场景,解决隧道防护门的可靠性问题,数据可以在采集器本地进行分析计算,降低对网络的依赖,并且具有较高的可扩展性与兼容性.

随着社会的进步与交通的快速发展,盾构机在我国的基础建设中占据着重要的地位,与其紧密的施工安全问题和智能化的问题也得到人们的密切关注.然而,我国在智能化盾构方面的理论知识和实际经验上相比于较发达国家尚有欠缺,盾构机运行时的参数主要还是依靠人工进行经验化的设置.为此,文中借鉴现在发展火热的人工智能和无人化汽车的经验,利用目前较成熟的数据处理和神经网络算法,利用各类传感器的实时数据对盾构下一步的参数进行预测.利用MLP神经网络和历史的盾构传感器数据对推进速度进行预测,可以得到与实际参数相比较为良好的预测结果集.由此仅对我国的盾构工程起到了很大的促进作用,也可减少人工经验化参数的设置,降低施工事故,使盾构施工更合理化、科学化.

提出了一种馈能型引纬机构,用于回收引纬过程中往复运动的冲击能量,根据振动理论对引纬机构的力学性能进行分析和台架试验.研究表明,结构的力学特性主要由梭摆质量、滑动阻尼、馈能弹簧刚度来体现,得到了相应的振动数值模型,并进行了数值模拟.试验结果表明,弹簧刚度设计合适,缓冲吸收效果非常明显,验证了数值模型的可靠性.

起重机在启动、停止时的摆动角度较大,影响工作效率,摆角检测系统的研究对于提高起重机的效率和实现起重机无人化的研究具有重要意义,文中设计了一种桥式起重机摆角检测系统,该系统使用图像传感器结合计算机视觉算法测得摆角.所提出的技术使用工业摄像头作为视觉传感器,基于YOLOv3目标检测算法识别吊钩,根据图像中吊钩离开平衡位置的位移和图像坐标变换计算出摆动角度.

Objective: As a carrier, bridge cranes are widely used in port cargo handling. Due to the structure of the crane itself, the load inevitably produces displacement deviation and lifting weight swing. Therefore, the optimization investigation of the bridge crane control system is carried out to guarantee the fast and secure operation of the crane. Method: According to the characteristics of bridge cranes, combined with the experience of on-site operating staff, the fuzzy rules that meet the working conditions of the site are designed by using the advantages of fuzzy PID (Proportion-Integration-Differentiation) control algorithm without relying on an accurate mathematical model, to set the parameters of the fuzzy PID controller. The speed control technique of frequency converter is adopted to design the system to improve the safety of the overall system. Results: The results show that the adjustment time of the swing angle of crane weight of the traditional PID control requires 6.79s, and the maximum swing angle of crane weight is 0.039rad, and the adjustment time under the fuzzy control is 4.89s, and the swing angle of crane weight is 0.013rad. The fuzzy PID controller surpasses the traditional PID controller in the positioning accuracy. It is not sensitive to changes in the internal parameters of the system. Also, it has strong adaptability to changes in operating conditions. The swing angle of crane weight is smaller, which further improves the robustness. The vector frequency conversion speed control has a more precise control effect on the speed and running track of the operating mechanism of the bridge crane. This method can be applied to the automatic operation mechanism of bridge cranes and achieve good control performance. Conclusion: The conducted optimization investigation of the bridge crane control system makes the control system flexible, simple and robust, providing important theoretical support for related explorations.

为了解决混凝土构件现有振动成型技术中密实度不高、成型周期长的问题.基于混沌理论对现有振动台结构进行改进,考虑碟形弹簧非线性建立振动系统的物理模型和动力学模型.通过分岔图确定系统产生混沌时的参数范围,利用Matlab/Simulink搭建混沌响应数值仿真平台,采用Runge-Kutta法求解并绘制系统的相轨迹图、Poincaré映射图、时间历程曲线、频谱图和最大Lyapunov指数对振动台的混沌特征进行识别,证明了改进后的系统具有混沌振动特性.

针对由PLC及变频器驱动的桥机系统加入防摇算法后在制动阶段防摇效果不明显的问题,从制动阶段计算速度曲线出发,分析了反向超调对制动阶段防摇效果的影响,提出了改变控制主导参数、分段减速制动、去除反向超调制动三种方法,在MATLAB及实际桥机中对三种方法进行了仿真及试验,结果表明去除反向超调制动方法能有效提高桥机在制动阶段的防摇效果.

With the development of information technology, intermodal transport research pays more attention to dynamic optimization and multi-role cooperation. The core issue of this paper was to realize container routing with dynamic adjustment, real-time optimization, and multi-role cooperation characteristics in the intermodal transport network. This paper first introduces the Intermodal Transport Cooperation Protocol (ITCP) that describes the operation and analysis of intermodal transport systems with the concept of encapsulation and layering. Then, a new network flow control method was built based on Model Predictive Control (MPC) in the ITCP framework. The method takes real-time information from all ITCP layers as input and generates flow control decisions for containers. To evaluate the method's effectiveness, a discrete event simulation experiment is applied. The results show that the proposed method outperforms the all-or-nothing method in scenarios with high freight volume, which means the method proposed in this paper can effectively balance the network transport load and reduce network operating costs. The research of this paper may throw some new light on intermodal transport research from the perspectives of digitization, multi-role cooperation, dynamic optimization, and system standardization.