Intelligent Metal Detection and Disposal Automation Equipment Based on Geometric Optimization Driving Algorithm

In order to solve the problem of metal impurities mixed in the production line of wood pulp nonwoven raw materials,intelligent metal detection and disposal automation equipment is designed.Based on the principle of electromagnetic induction,the precise positioning of metal coordinates is realized by initial inspection and multi-directional re-inspection.Based on a geometry optimization driving algorithm,the cutting area is determined by locating the center of the circle that covers the maximum area.This approach aims to minimize the cutting area and maximize the use of materials.Additionally,the method strives to preserve as many fabrics at the edges as possible by employing the farthest edge covering circle algorithm.Based on a speed compensation algorithm,the flexible switching of upper and lower rolls is realized to ensure the maximum production efficiency.Compared with the metal detection device in the existing production line,the designed automation equipment has the advantages of higher detection sensitivity,more accurate metal coordinate positioning,smaller cutting material areas and higher production efficiency,which can make the production process more continuous,automated and intelligent.

Review of advanced road materials, structures, equipment, and detection technologies

As a vital and integral component of transportation infrastructure,pavement has a direct and tangible impact on socio-economic sustainability.In recent years,an influx of groundbreaking and state-of-the-art materials,structures,equipment,and detection technologies related to road engineering have continually and progressively emerged,reshaping the landscape of pavement systems.There is a pressing and growing need for a timely summarization of the current research status and a clear identification of future research directions in these advanced and evolving technologies.Therefore,Journal of Road Engineering has undertaken the significant initiative of introducing a comprehensive review paper with the overarching theme of“advanced road materials,structures,equipment,and detection technologies”.This extensive and insightful review meticulously gathers and synthesizes research findings from 39 distinguished scholars,all of whom are affiliated with 19 renowned universities or research institutions specializing in the diverse and multidimensional field of highway engineering.It covers the current state and anticipates future development directions in the four major and interconnected domains of road engineering:advanced road materials,advanced road structures and performance evaluation,advanced road construction equipment and technology,and advanced road detection and assessment technologies.

Multi-Equipment Detection Method for Distribution Lines Based on Improved YOLOx-s

The YOLOx-s network does not sufficiently meet the accuracy demand of equipment detection in the autonomous inspection of distribution lines by Unmanned Aerial Vehicle(UAV)due to the complex background of distribution lines,variable morphology of equipment,and large differences in equipment sizes.Therefore,aiming at the difficult detection of power equipment in UAV inspection images,we propose a multi-equipment detection method for inspection of distribution lines based on the YOLOx-s.Based on the YOLOx-s network,we make the following improvements:1)The Receptive Field Block(RFB)module is added after the shallow feature layer of the backbone network to expand the receptive field of the network.2)The Coordinate Attention(CA)module is added to obtain the spatial direction information of the targets and improve the accuracy of target localization.3)After the first fusion of features in the Path Aggregation Network(PANet),the Adaptively Spatial Feature Fusion(ASFF)module is added to achieve efficient re-fusion of multi-scale deep and shallow feature maps by assigning adaptive weight parameters to features at different scales.4)The loss function Binary Cross Entropy(BCE)Loss in YOLOx-s is replaced by Focal Loss to alleviate the difficulty of network convergence caused by the imbalance between positive and negative samples of small-sized targets.The experiments take a private dataset consisting of four types of power equipment:Transformers,Isolators,Drop Fuses,and Lightning Arrestors.On average,the mean Average Precision(mAP)of the proposed method can reach 93.64%,an increase of 3.27%.The experimental results show that the proposed method can better identify multiple types of power equipment of different scales at the same time,which helps to improve the intelligence of UAV autonomous inspection in distribution lines.

Online Fault Detection Configuration on Equipment Side of a Variable-Air-Volume Air Handling Unit

With the development of the technology of the Internet of Things,more and more operational data can be collected from air conditioning systems.Unfortunately,the most of existing air conditioning controllers mainly provide controlling functions more than storing,processing or computing the measured data.This study develops an online fault detection configuration on the equipment side of air conditioning systems to realize these functions.Modbus communication is served to collect real-time operational data.The calculating programs are embedded to identify whether the measured signals exceed their limits or not,and to detect if sensor reading is frozen and other faults in relation to the operational performance are generated or not.The online fault detection configuration is tested on an actual variable-air-volume(VAV)air handling unit(AHU).The results show that the time ratio of fault detection exceeds 95.00%,which means that the configuration exhibits an acceptable fault detection effect.

Similarity evaluation model for the internal defect detection of strip steel

An internal defect meter is an instrument to detect the internal inclusion defects of cold-rolled strip steel.The detection accuracy of the equipment can be evaluated based on the similarity of the multiple detection data obtained for the same steel coil.Based on the cosine similarity model and eigenvalue matrix model,a comprehensive evaluation method to calculate the weighted average of similarity is proposed.Results show that the new method is consistent with and can even replace artificial evaluation to realize the automatic evaluation of strip defect detection results.

An Automated Detection Approach of Protective Equipment Donning for Medical Staff under COVID-19 Using Deep Learning

Personal protective equipment(PPE)donning detection for medical staff is a key link of medical operation safety guarantee and is of great significance to combat COVID-19.However,the lack of dedicated datasets makes the scarce research on intelligence monitoring of workers’PPE use in the field of healthcare.In this paper,we construct a dress codes dataset for medical staff under the epidemic.And based on this,we propose a PPE donning automatic detection approach using deep learning.With the participation of health care personnel,we organize 6 volunteers dressed in different combinations of PPE to simulate more dress situations in the preset structured environment,and an effective and robust dataset is constructed with a total of 5233 preprocessed images.Starting from the task’s dual requirements for speed and accuracy,we use the YOLOv4 convolutional neural network as our learning model to judge whether the donning of different PPE classes corresponds to the body parts of the medical staff meets the dress codes to ensure their self-protection safety.Experimental results show that compared with three typical deeplearning-based detection models,our method achieves a relatively optimal balance while ensuring high detection accuracy(84.14%),with faster processing time(42.02 ms)after the average analysis of 17 classes of PPE donning situation.Overall,this research focuses on the automatic detection of worker safety protection for the first time in healthcare,which will help to improve its technical level of risk management and the ability to respond to potentially hazardous events.

Real-Time Safety Helmet Detection Using Yolov5 at Construction Sites

The construction industry has always remained the economic and social backbone of any country in the world where occupational health and safety(OHS)is of prime importance.Like in other developing countries,this industry pays very little,rather negligible attention to OHS practices in Pakistan,resulting in the occurrence of a wide variety of accidents,mishaps,and near-misses every year.One of the major causes of such mishaps is the non-wearing of safety helmets(hard hats)at construction sites where falling objects from a height are unavoid-able.In most cases,this leads to serious brain injuries in people present at the site in general and the workers in particular.It is one of the leading causes of human fatalities at construction sites.In the United States,the Occupational Safety and Health Administration(OSHA)requires construction companies through safety laws to ensure the use of well-defined personal protective equipment(PPE).It has long been a problem to ensure the use of PPE because round-the-clock human monitoring is not possible.However,such monitoring through technological aids or automated tools is very much possible.The present study describes a systema-tic strategy based on deep learning(DL)models built on the You-Only-Look-Once(YOLOV5)architecture that could be used for monitoring workers’hard hats in real-time.It can indicate whether a worker is wearing a hat or not.The proposed system usesfive different models of the YOLOV5,namely YOLOV5n,YOLOv5s,YOLOv5 m,YOLOv5l,and YOLOv5x for object detection with the support of PyTorch,involving 7063 images.The results of the study show that among the DL models,the YOLOV5x has a high performance of 95.8%in terms of the mAP,while the YOLOV5n has the fastest detection speed of 70.4 frames per second(FPS).The proposed model can be successfully used in practice to recognize the hard hat worn by a worker.

Device Anomaly Detection Algorithm Based on Enhanced Long Short-Term Memory Network

The problems in equipment fault detection include data dimension explosion,computational complexity,low detection accuracy,etc.To solve these problems,a device anomaly detection algorithm based on enhanced long short-term memory(LSTM)is proposed.The algorithm first reduces the dimensionality of the device sensor data by principal component analysis(PCA),extracts the strongly correlated variable data among the multidimensional sensor data with the lowest possible information loss,and then uses the enhanced stacked LSTM to predict the extracted temporal data,thus improving the accuracy of anomaly detection.To improve the efficiency of the anomaly detection,a genetic algorithm(GA)is used to adjust the magnitude of the enhancements made by the LSTM model.The validation of the actual data from the pumps shows that the algorithm has significantly improved the recall rate and the detection speed of device anomaly detection,with the recall rate of 97.07%,which indicates that the algorithm is effective and efficient for device anomaly detection in the actual production environment.

浅析ARIES采集站Equipment not detected故障的维修

在ARIES采集站的测试、检修过程中,比较常见的故障是Equipment not detected(检测不到设备)的故障。本文根据Equipment not detected出现时的故障现象,有针对性地分析了放电接口板被烧毁、采集站电源异常、不能正常传输时对应的电源电路,准确地定位了故障的原因,并介绍了相应的故障排除方法。

The Development of a Knowledgeable Monitoring System for Weapon Equipment

In order to make the combat effectiveness of weapon yield well, it is necessary to maintain and monitor the weapons. Based on the requirement of weapon equipment, an integrated maintaining system is developed, which can be used to carry out the maintenance by using electricity periodically and function detection at module level. So, the breakdown rate of product component is deceased a lot, product reliability is improved, and the life cycle is prolonged. Thus, combat effectiveness of weapon equipment is improved.

GNSS/MET Station Equipment Maintenance and Product Application

Based on the use of Global Navigation Satellite System (GNSS) for meteorological detection in the world, we used the GNSS/MET detection equipment in the meteorological departments of Liaoning Province of China and its data to study and summarize the maintenance methods of GNSS/MET (Global Navigation Satellite System Meteorology) detection equipment and the application of water vapor products in operational systems. The results show that: 1) For GNSS/MET failures, specific inspections and classifications can be performed according to different phenomena;2) The GNSS water vapor measurement station samples every 30 seconds, forming one set of GNSS data every hour, and can detonate the atmospheric precipitation by solving the original data;3) Using the “Navigation Satellite Remote Sensing Water Vapor Application Management System”, the GNSS/MET water vapor products can be directly displayed. We can get the conclusion that GNSS/MET has far-reaching significance for studying the law of atmospheric water vapor changes and enhancing the ability to monitor severe weather such as heavy rain and strong convection.

Measurements &Detection Techniques in Nanotechnology in Road Applications

In recent years, the world of science has started to produce advanced materials and technology in the nanoscale, which known as nanotechnology. The use of nanotechnology has become wide spread in all branches of science, one of the important branches is the field of transportation. The application of nanotechnology in pavements showed great promise and the potential to change commonly used materials, which makes transportation more efficient, smart looking, stronger and durable that all lead to the extension of their life cycle of the roads. So, there is an essential need to prepare advanced nanotechnology tools and detection systems contain very recent instruments needed for nanotechnology studies, since the physical, chemical and biological properties of the material at nanoscale differ in fundamental and valuable ways from that at normal scale. In this work the different techniques in measuring and detection techniques in nanotechnology will be discussed the method of operation and accuracy of each technique will be evaluated, the main applications of each technique in industrial and construction field will be evaluated.

基于改进YOLOv7-tiny的高空作业人员安防装备检测算法

针对现有高空作业人员安防装备检测算法参数量较大且检测目标相对单一,难以适应复杂的高空作业场景等问题,提出一种基于改进YOLOv7-tiny的高空作业人员安防装备检测算法。首先,将主干网络重新设计为更轻量的YOLOv7-FasterNet,并调整空间金字塔池化结构,实现模型参数量的压缩;其次,在ELAN-L模块中扩展梯度传输路径的分支,解决了模型压缩造成的通道信息缺失问题,提升了特征信息的提取能力;最后,将网络中下采样部分替换为Involution模块,降低参数冗余,增强网络对全局的捕获能力。实验结果表明,改进的YOLOv7-tiny算法能够更好地适应复杂高空作业场景,在开源数据集上具备良好的性能。该算法的平均检测精度达到94.7%,较原模型提升1.5%,参数量较原模型下降11.6%,实验结果验证了算法改进措施的有效性。

基于tSNE多特征融合的JTC轨旁设备故障检测

无绝缘轨道电路(Jointless Track Circuit,JTC)的轨旁设备在室外长期运营过程中,其可靠性会逐渐降低,进而给列车行车安全带来严重威胁。以轨道电路读取器(Track Circuit Reader,TCR)感应电压为基础,针对JTC故障诊断研究中轨旁设备故障类型复杂和故障特征提取不充分等问题,提出一种基于t分布随机邻域嵌入(t-distribution Stochastic Neighbor Embedding,tSNE)多特征融合的JTC轨旁设备故障检测模型。首先,根据不同轨旁设备故障对TCR感应电压信号的影响,分析各轨旁设备的故障特性。其次,提取TCR感应电压信号的方差、有效值、峰值因子等幅值域特征,以及排列熵、散布熵特征构成原始故障特征集。为了去除其中的冗余信息,得到具有较高判别性的融合流形特征,利用tSNE算法进行特征融合。最后输入深度残差网络(Deep Residual Network,DRN)得到故障检测混淆矩阵,实现轨旁设备故障定位。实验结果表明:tSNE算法融合后的特征在异类和同类故障样本之间分别有较大的类间间距和较小的类内间距,相比主成分分析(Principal Component Analysis, PCA)、随机相似性嵌入(Stochastic Proximity Embedding, SPE)、随机邻域嵌入(Stochastic Neighbor Embedding,SNE)算法具有更优的融合特征提取效果。此外,结合DRN可以有效识别多种轨旁设备故障,达到98.28%的故障检测准确率。通过现场信号进行实例验证,结果表明该故障检测模型能满足铁路现场对室外设备进行故障定位的实际需求。

基于轻量化YOLOv5的电气设备外部缺陷检测

为了提高变电站电气设备外部缺陷实时检测的精度,同时让检测模型更加轻量化,提出了一种基于轻量化YOLOv5的电气设备外部缺陷检测方法。首先,构建电气设备外部缺陷图像数据集并进行数据增强处理。其次,采用3种优化策略对原YOLOv5进行改进:通过引入EfficientViT网络改进算法主干网络,减少模型参数量,并在算法Neck部分中加入SimAM无参数注意力机制来提高变电站复杂背景下的识别精度,同时采用Soft-NMS模块来改进检测框筛选方式,避免出现缺陷漏检现象。最后,通过消融实验进行验证。结果表明:轻量化后的电气设备外部缺陷检测模型mAP值稳定在86.4%,与原模型相比提高了1.2百分点,模型参数量减少了20%,计算量减少了38%,模型大小为11 MB,比原模型减少了19.7%。改进后的模型能够满足设备外部缺陷实时检测的要求,可以实现模型的轻量化部署。

基于FreeRTOS的无线便携式多气体检测仪设计

本文提出了一种基于FreeRTOS的无线便携式多气体检测仪,旨在解决矿山井下环境中有毒有害气体检测面临的复杂挑战。这些挑战包括检测任务繁重、数据存储受限和数据传输不便等问题。为了应对这些问题,本文采用了STM32单片机作为核心处理单元,并结合多种气体传感器进行目标气体的检测。检测仪通过模拟-数字转换(ADC)模块对传感器信号进行采样,并应用卡尔曼滤波算法对采集到的原始数据进行滤波处理,从而提高了数据的准确性,并有效减少了噪声对检测结果的影响。在数据传输方面,采用高速传输且低功耗的WiFi无线通信技术与云平台实现信息交互,最终实现了对检测数据的存储和可视化展示。这一功能不仅提升了数据处理的效率,还方便了远程查看和数据分析。为了进一步提升系统的可靠性和稳定性,检测仪植入了FreeRTOS嵌入式实时操作系统,利用系统的多线程特性,实现对CH_(4)、CO和H_(2)S三种气体的同时检测,显著降低了检测时数据的丢失率。测试结果表明,该检测仪具备了优良的便携性、高精度和良好的稳定性。其设计有效地提高了矿山井下有毒有害气体检测的效率,为矿山安全管理提供了一种先进的解决方案。该检测仪还具有显著的应用前景和推广价值,能够显著提升矿山作业的安全性。总体而言,基于FreeRTOS的无线便携式多气体检测仪为矿山井下环境中的有毒有害气体检测提供了一种高效、可靠的技术手段。

基于物联网技术的二氧化碳检测仪开发

为了有效解决校准培养箱中二氧化碳气体浓度因管线连接可能引发的气密性问题,研制了一种物联网二氧化碳气体检测仪。集成了低功耗蓝牙技术,能将传感器采集的测量数据实时传输至互联网微信小程序终端,为物联网仪器的开发提供了一种信的思路与方法。测试结果显示,所开发的二氧化碳检测仪的体积分数测量范围为0~20%,引用示值误差控制在其满量程的0.5%以内,满足相关校准规范对标准器的技术要求,为在二氧化碳培养箱中进行二氧化碳浓度校准时选择合适的标准器提供了可靠的解决方案。

基于大数据的工业设备智能检测与远程运维系统研究

设备的性能及运行的可靠性是企业提高生产效率、降低运营成本的关键。基于某汽车整车厂2017-2019年度的维修统计数据,通过对设备劣化分析、故障类型分类、故障预测性解决方案以及AI技术在故障检测中的应用等内容,深入研究了设备故障机理及运维技术,并提出了基于大数据的设备智能检测与远程运维系统的设计方案。通过远程实时监控、智能检测设备的运行状态、通过大数据分析,发现设备运行潜在的安全风险,预测并预防可能的故障,以提高设备的可靠性、降低运维成本并提升运维效率,为企业的可持续发展提供有力保障。

炼化装置老旧设备安全保障一体化技术应用研究

针对当前国内炼化装置设备存在的“老龄化”趋势,以及由此引起的安全隐患问题,基于当前所存在的不足,提出基于损伤模式识别、安全评估并结合全面完备的在线检测(监测)等手段的炼化装置老旧设备安全保障一体化的技术路线,并将此技术路线应用于某大型石化企业中。结果表明:应用该技术路线可找出老旧设备的安全隐患,可提升企业针对老旧设备的管理水平。该技术路线可为当前炼化装置老旧设备所面临的困难提供技术指导。

基于整体与局部纹理特征加权融合的港机装备钢丝绳断丝缺陷检测研究

钢丝绳是港机装备的重要部件,由于作业工况环境恶劣,钢丝绳表面极易引起断丝等缺陷,影响港机装备作业安全。针对港机装备钢丝绳表面油泥严重、光照亮度不均,以及港机装备钢丝绳断丝缺陷能够在钢丝绳股顶钢丝椭圆形区域内有效体现的特点,提出了一种基于钢丝绳整体纹理特征与股顶钢丝椭圆形区域边缘轮廓纹理特征加权融合的钢丝绳断丝缺陷检测方法。首先采用图像降噪、增强、校正技术对钢丝绳原始图像进行预处理。然后采用图像平滑、阈值分割及边缘特征提取技术对股顶钢丝椭圆形区域边缘轮廓进行提取。接着采用局部二值模式(LBP)算子分别提取钢丝绳整体纹理特征与股顶钢丝椭圆形区域边缘轮廓纹理特征,并对钢丝绳整体纹理及边缘轮廓纹理特征进行特征加权融合。最后对加权融合后的特征向量进行主成分分析(PCA)法降维,并应用支持向量机(SVM)技术对钢丝绳断丝缺陷进行检测。研究结果表明:本文提出的方法对实际工况下重油泥、光照不均等钢丝绳断丝缺陷具有较好的检测效果,具有一定的工程应用价值。