Distributed process monitoring based on Kantorovich distancemultiblock variational autoencoder and Bayesian inference

Modern industrial processes are typically characterized by large-scale and intricate internal relationships.Therefore,the distributed modeling process monitoring method is effective.A novel distributed monitoring scheme utilizing the Kantorovich distance-multiblock variational autoencoder(KD-MBVAE)is introduced.Firstly,given the high consistency of relevant variables within each sub-block during the change process,the variables exhibiting analogous statistical features are grouped into identical segments according to the optimal quality transfer theory.Subsequently,the variational autoencoder(VAE)model was separately established,and corresponding T^(2)statistics were calculated.To improve fault sensitivity further,a novel statistic,derived from Kantorovich distance,is introduced by analyzing model residuals from the perspective of probability distribution.The thresholds of both statistics were determined by kernel density estimation.Finally,monitoring results for both types of statistics within all blocks are amalgamated using Bayesian inference.Additionally,a novel approach for fault diagnosis is introduced.The feasibility and efficiency of the introduced scheme are verified through two cases.

Method of Monitoring Wearing and Breakage States of Cutting Tools Based on Mahalanobis Distance Features

The Mahalanobis distance features proposed by P.C.Mahalanobis, an Indian statistician, can be used in an automatic on-line cutting tool condition monitoring process based on digital image processing. In this paper, a new method of obtaining Mahalanobis distance features from a tool image is proposed. The key of calculating Mahalanobis distance is appropriately dividing the object into several component sets. Firstly, a technique is proposed that can automatically divide the component groups for calculating Mahalanobis distance based on the gray level of wearing or breakage regions in a tool image. The wearing region can be divided into high gray level component group and the tool-blade into low one. Then, the relation between Mahalanobis distance features of component groups and tool conditions is investigated. The results indicate that the high brightness region on the flank surface of the turning tool will change with its abrasion change and if the tool is heavily abraded, the area of high brightness will increase apparently. The Mahalanobis distance features of high gray level component group are related with wearing state of tool and low gray level component group correlated with breakage of tool. The experimental results show that the abrasion of the tool’s flank surface affected the Mahalanobis distances of high brightness component of the tool and the pixels of high brightness component set. Compared with the changes of them, we found that the Mahalanobis distance of high brightness component of the tool was more sensitive to the abrasion of cutting tool than the area of high brightness component set of the tool. Here we found that the relative changing rate of the area of high brightness component set was not quite obvious and it was ranging from 2% to 15%, while the relative changing rate of the Mahalanobis distance in table 1 ranges from 13.9% to 47%. It is 3 times higher than the changing rate of the area.

Hellinger distance based probability distribution approach to performance monitoring of nonlinear control systems

Control performance monitoring has attracted great attention in both academia and industry over the past two decades. However, most research efforts have been devoted to the performance monitoring of linear control systems, without considering the pervasive nonlinearities(e.g. valve stiction) present in most industrial control systems. In this work, a novel probability distribution distance based index is proposed to monitor the performance of non-linear control systems. The proposed method uses Hellinger distance to evaluate change of control system performance. Several simulation examples are given to illustrate the effectiveness of the proposed method.

Face Mask and Social Distance Monitoring via Computer Vision and Deployable System Architecture

The coronavirus(COVID-19)is a lethal virus causing a rapidly infec-tious disease throughout the globe.Spreading awareness,taking preventive mea-sures,imposing strict restrictions on public gatherings,wearing facial masks,and maintaining safe social distancing have become crucial factors in keeping the virus at bay.Even though the world has spent a whole year preventing and curing the disease caused by the COVID-19 virus,the statistics show that the virus can cause an outbreak at any time on a large scale if thorough preventive measures are not maintained accordingly.Tofight the spread of this virus,technologically developed systems have become very useful.However,the implementation of an automatic,robust,continuous,and lightweight monitoring system that can be efficiently deployed on an embedded device still has not become prevalent in the mass community.This paper aims to develop an automatic system to simul-taneously detect social distance and face mask violation in real-time that has been deployed in an embedded system.A modified version of a convolutional neural network,the ResNet50 model,has been utilized to identify masked faces in peo-ple.You Only Look Once(YOLOv3)approach is applied for object detection and the DeepSORT technique is used to measure the social distance.The efficiency of the proposed model is tested on real-time video sequences taken from a video streaming source from an embedded system,Jetson Nano edge computing device,and smartphones,Android and iOS applications.Empirical results show that the implemented model can efficiently detect facial masks and social distance viola-tions with acceptable accuracy and precision scores.

Large-scale deformation monitoring in mining area by D-InSAR and 3D laser scanning technology integration

Large-scale deformation can not be detected by traditional D-InSAR technique because of the limit of its detectable deformation gradient,we propose a method that combines SAR data with point cloud data obtained by 3D laser scanning to improve the gradient of deformation detection.The proposed method takes advantage of high-density of 3D laser scanning point cloud data and its high precision of point positioning after 3D modeling.The specifc process can be described as follows:frst,large-scale deformation points in the interferogram are masked out based on interferometric coherence;second,the interferogram with holes is unwrapped to obtain a deformation map with holes,and last,the holes in the deformation map are flled with point cloud data using inverse distance weighting algorithm,which will achieve seamless connection of monitoring region.We took the embankment dam above working face of a certain mining area in Shandong province as an example to study large-scale deformation in mining area using the proposed method.The results show that the maximum absolute error is 64 mm,relative error of maximum subsidence value is 4.95%,and they are consistent with leveling data of ground observation stations,which confrms the feasibility of this method.The method we presented provides new ways and means for achieving large-scale deformation monitoring by D-InSAR in mining area.

基于GIS的灌区土壤投入品残留污染监测预警系统

为了解决工业发展导致的灌区土壤投入品残留污染问题,给出一种基于地理信息系统(geographic information system,GIS)的土壤污染监测预警系统。该系统结合VOC-PF1型传感器、STM32主控芯片和GSM通信模块,实现了高效的数据采集和通信功能。通过反距离加权(inverse distance weighted,IDW)插值法进行空间分析,并设立预警阈值,实现对灌区土壤投入品残留污染的实时监测和预警。实验结果表明:该系统的监测精度高达98%,监测时长最高为49 s,具有很高的实用性和效率。研究结果不仅为灌区土壤投入品残留污染监测提供了有效手段,也为环境保护和农业可持续发展提供有力支持。

基于时间序列压缩分割的监测数据异常识别算法研究

为有效识别桥梁健康监测数据的异常,减少误预警、漏预警现象,保障桥梁监测数据的质量和有效性,针对大跨度斜拉桥长期监测数据的缺失、离群和漂移3类异常数据,提出基于时间序列压缩分割的监测数据异常识别算法。该算法将原始监测数据时间序列通过基于序列重要点(Series Importance Point, SIP)的时间序列线性分段(Piecewise Linear Represent, PLR)算法(PLR_SIP)得到数条时间子序列;然后采用欧氏距离进行时间子序列的相似性分析,并基于改进的局部离群因子(Local Outlier Factor, LOF)算法计算每条时间子序列的局部离群因子;最后将其与设定的阈值相比较,从而识别出监测数据的异常。为验证该算法的准确性与工程实用性,对某公路大跨度斜拉桥健康监测数据进行异常识别。结果表明:采用PLR_SIP算法对原始时间序列压缩分割得到的时间子序列能够准确地反映原序列的变化趋势和范围;改进的LOF算法突破了传统LOF算法仅能识别离群值这类无持续时间异常的局限性,能够排除噪声的干扰,实现对离群、缺失和漂移3种异常的识别。该算法无需定义训练集,直接以原始监测数据作为算法的输入,同时能够自适应调整阈值参数,具有良好的可扩展性、实时性、准确性和高效性,适用于处理实时、大量的桥梁健康监测数据。

刍议坝料超长距离运输管理模式

基于李家岩水库工程现场运输管理中遇到的问题,为提高坝料超长距离运输的安全性,项目部制定了一套坝料超长距离运输管理模式,引入了一套依靠北斗定位导航设计的、可在汽车运输时准确进行调控的系统,其可以有效地实时追踪和管理大坝建设过程中坝料的运输情况,进而提升了运输效率并可实时掌握与管理整个坝料的运输循环周期。阐述了对坝料超长距离运输管理模式进行的研究。

基于密度聚类的复杂装备健康监测方法

针对复杂装备历史数据往往存在非球形的特征,提出了一种基于密度聚类的复杂装备健康监测模型。从历史数据中估计各个样本的局部密度和类间距离,并综合考虑两者的统计特性以确定数据的聚类中心,对于新采集的复杂装备健康状态监测数据,如果它与聚类中心密度可达,就认为该复杂装备处于健康状态,否则就处于非健康状态。通过数值仿真技术分析了一个实际的复杂装备数据集,以及利用散点图、盒图和平行坐标系等可视化技术来验证计算结果的可靠性,仿真结果表明提出的方法能够有效监测复杂装备的健康状态。

基于奇异值分解的马氏距离光伏故障监测

及时检测出光伏阵列短路、断路、阴影遮挡等故障并作出相应处理,不仅有利于提高光伏阵列收益,也有利于降低光伏阵列安全风险,避免火灾等事故。基于马氏距离方法,计算出光伏阵列实测电流电压和仿真模型输出电流电压的相似程度,以此用于光伏阵列故障监测。正常条件下,光伏阵列实测电流电压值和仿真模型输出电流电压值较为接近,传统马氏距离方法在求解逆矩阵时将产生奇异解。结合奇异值分解方法对传统马氏距离方法进行了改进,试验证明,所提出的方法能够精准检测出相应的短路、断路、阴影遮挡等故障。

一种高空坠楼监测预警系统研究

针对儿童高空坠楼问题,本文利用目标检测与目标测距技术设计了一种高空坠楼监测预警系统.系统改善了现有YOLO目标检测算法模型过大参数量过多的问题,同时还借助单目摄像头测距方法设计了一种新的危险距离测量算法.实验结果表明,改进后的目标检测算法在保证其他参数相差不大的情况下,model size与比原来的YOLOv8n降低了1.99 MB,parameters比原始模型降低了1.039×109.新设计的危险距离测量算法在4个不同场景100组数据中横向误差均值为2.29 cm,纵向误差均值为3.16 cm,危险监测预警准确率92%.综合说明,本文设计的高空坠楼检测预警系统准确性高、实用性强,有较高的社会应用价值.

极小净距隧道先行洞振动响应特性及衰减规律研究

隧道是铁路等交通设施的重要组成部分,研究隧道爆破开挖过程中的振动响应和衰减规律对隧道建设具有重要意义。以渝昆高铁线路建设为背景,研究了高铁隧道爆破开挖时面临极小净距问题的爆破方案,提出了将掘进断面分为左右两侧交替爆破的开挖方式,并对双线隧道进行了爆破振动速度监测。对先行洞振动速度分析表明:隧道内最大振动速度主要由掏槽孔及拱顶辅助孔爆破产生,并且径向振动速度最大;横断面上,迎爆侧边墙拱腰振速为拱脚振速的1.3倍至2倍,且由掏槽孔起爆引起的该振速倍率相对较小;纵断面上,掌子面前方各测点振动速度大于后方相对位置处振速,而后方振速衰减速率相对更高。研究结果已成功应用于现场工程实际,相关隧道小净距段已安全贯通,可对类似极小净距隧道工程提供一定的借鉴参考意义。

具有距离监测与辅助提示功能的自闭症儿童智能服装设计

针对自闭症儿童社交与情绪障碍问题,设计了2款具备距离监测与辅助提示功能的自闭症儿童智能服装。首先,采用问卷调查与实地访谈等方法,归纳出自闭症儿童在穿戴、感统、心理认同3个方面的服装需求;其次,采用超声波距离传感器技术实现社交距离监测;再次,采用气动柔性气囊及可加热碳纤维模拟中医推拿治疗自闭症手法,加热加压刺激手腕部穴位,达到一定的干预和辅助提示患者的目标。研究结果表明:选用的超声波传感器精度高,形状小巧且耐脏污,将其嵌入服装中可有效识别0.2~3 m范围内的社交距离;通过柔性气囊的充气加压可对手腕部穴位产生7.90 kPa的服装压,有助于缓解患者情绪焦虑等症状;智能服装廓形设计采用H/A型,开合方式选用拉链,色彩选用冷色调,口袋设计为几何图形,面料设计兼具舒适性与功能性,符合自闭症儿童服装实际穿着需求。

引入时距信息的IR-UWB雷达多域特征融合呼吸模式识别方法

人体呼吸系统相关疾病常常伴随着呼吸深度和节律的异常,因此呼吸信号监测和呼吸模式识别在医疗健康领域中尤其是对于睡眠监测、疾病预断具有重要意义。其中,非接触式的脉冲式超宽带雷达(Impulse Radio Ultra-Wideband,IR-UWB)因具有良好的距离分辨率和穿透能力以及全天候全天时、安全无创的检测优势,正逐步成为睡眠健康监护领域中最关键的感知技术之一。然而受睡眠监测特定的室内场景影响,复杂的测量环境给呼吸模式特征的准确提取带来了限制和挑战,传统的雷达呼吸模式识别算法主要关注一维呼吸时、频域特征,而IR-UWB雷达目标回波信息分散在多个距离门内,使用一维特征识别准确率较低。为此,本文针对IR-UWB雷达中人体呼吸在时间上慢速起伏运动、在距离上是扩展目标的信号模型特点,提出了一种引入时距信息的IR-UWB雷达多域特征融合呼吸模式识别方法。算法在提取一维呼吸信号波形时、频域特征的基础上更进一步挖掘雷达二维时距图像中潜在的呼吸模式形态特征,通过多域特征融合实现呼吸模式的非接触式检测和识别。在图像处理上,针对图像受呼吸异常节律影响呈现局部粘连特性导致呼吸周期提取难的问题,提出一种通过相位矩阵图像处理来检测雷达图像中的呼吸时距条带从而获取图像特征的方法。实验结果表明,利用该算法提取的多域特征对六种呼吸模式进行机器学习的分类识别,可以实现96.3%的识别准确率。

基于Hausdorff距离的光伏漏电流监测方法

与带有隔离变压器的大型光伏系统相比,分布式光伏的功率与体积较小,且为了提高工作效率,在设计时多采用非隔离型逆变器,但也因此产生了会危害配网系统安全的光伏漏电流。以单相全桥结构的光伏系统为例,详细分析了光伏漏电流的产生机理,随后分析了光伏漏电流进入配电网后的波形特征,并在此基础上提出了一种基于Hausdorff距离的光伏漏电流监测方法,简单高效地解决了光伏漏电流监测与保护困难的问题。最后对提出的方法进行仿真分析,验证了所提方法的可行性。

长距离输水管道自动化监控系统的设计与研究

为实现提高长距离输水管道远程调度、智能监控及安全监测之目的,本文在南水北调“北三县”供水工程中引入自动监控、视频监控、分布式光纤传感技术及智能井盖监测技术,并重点介绍了综合管控平台应用管理中管网漏损监测子系统与智能井盖监测子系统的工作原理。在综合管控信息化平台大数据的支撑下,实现了对供水工程内全要素及供水调度管理全过程的精准化、智能化监控,特别是,解决了长距离管网漏损的预警监测、故障定位及阀井井盖的实时状态监测、有限空间监控,对于供水工程的安全运行具有重大意义。

长距离地铁隧道自动化监测技术工程实践与应用

随着地铁保护工程的监测范围越来越长,工况越来越复杂,难度越来越高。长距离(>400 m)的监测范围,多台(>3)仪器串联的项目也屡见不鲜,对自动化监测技术的要求也越来越高。通过某项目涉及杭州地铁1号线保护监测的工程实践,阐述了5+2台测量机器人串联系统的监测技术实施及数据分析,证明了多台测量机器人串联自动化监测技术的准确性及可靠性,为后续同类型项目提供借鉴。

GNSS露天矿监测中观测距离、时间与精度的建模分析

GNSS在线监测具有全天候监测的技术优势,已被广泛应用于露天矿边坡监测。其观测精度受观测时长及监测站至基准站距离的制约,建立观测时长、监测站至基准站距离与观测精度的回归模型能有效确定合理观测时长。本文以鞍钢露天矿GNSS在线监测数据为数据源,分析了不同观测距离、时长对精度的影响规律,利用多元非线性回归方法建立了精度与距离、时长的三因素回归模型。其中,精度拟合模型在N、E、U方向的三因素相关性分别为0.97、0.96、0.97,检验模型精度分别为0.33 mm、0.30 mm、0.17 mm;时间拟合模型在N、E、U方向的三因素相关性分别为0.96、0.93、0.93,检验模型精度分别为0.14 h、0.06 h、0.08 h。研究结果表明,该方法能以精度和距离为约束条件,为基于GNSS露天矿边坡在线监测预警观测时长的确定提供依据。

基于欧氏距离的胎儿监护仪数据异常检测方法

为满足医院对胎儿监护仪数据异常检测的准确性需求,提出基于欧氏距离的医院胎儿监护仪数据异常检测方法。根据医院胎儿监护仪数据异常检测方法,应用超声多普勒检测仪采集胎儿心率信号数据,采用基于自适应滤波的胎儿心率信号去噪方法去除所采集胎儿心率信号噪声成分,将去噪后的胎儿心率信号作为基于欧氏距离法的胎儿心率信号异常识别方法的识别样本,结合欧氏距离法与强分类器,分析去噪后胎儿心率信号与正常胎儿心率信号的虚拟正弦曲线欧氏距离是否大于整定的欧氏距离阈值,将大于阈值的胎儿心率信号样本分类诊断为异常监护数据。该方法通过医院胎儿监护仪采集的胎儿心率信息准确,且异常检测结果有效可信。

近距离煤层采空区下淋水巷道稳定性分析

为研究近距离煤层采空区下淋水巷道稳定性,以石槽村矿220605工作面运输巷为研究背景,通过实验室测试,现场监测与数值模拟相结合方法,开展淋水顶板巷道稳定性研究。研究结果表明:(1)淋水巷道顶板黏土矿物中富含高岭石、蒙脱石、伊利石以及绿泥石,遇水后会迅速膨胀并逐渐流变破碎,从而导致围岩滑移。(2)水软化岩石初期,其峰值强度显著减小,且在破坏前、后表现出波浪式的承载特性。顶板岩石含水率从干燥增长至近饱和状态时,抗压强度、弹性模量、内摩擦角、黏聚力分别减小88.56%、62.45%、33.81%和58.54%。(3)水作用下的巷道塑性破坏主要为剪切破坏;随着围岩含水率的增大,顶底板塑性破坏范围变化不大,但顶板在水软化效应的承压能力不断削弱,两帮间接承压力明显提升,塑性破坏范围变化趋势为先线性增大而后趋于稳定。