Smart industrial IoT empowered crowd sensing for safety monitoring in coal mine

The crowd sensing technology can realize the sensing and computing of people,machines,and environment in smart industrial IoT-based coal mine,which provides a solution for safety monitoring through distributed intelligence optimization.However,due to the difficulty of neural network training to achieve global optimality and the fact that traditional LSTM methods do not consider the relationship between adjacent machines,the accuracy of human body position prediction and pressure value prediction is not high.To solve these problems,this paper proposes a smart industrial IoT empowered crowd sensing for safety monitoring in coal mine.First,we propose a Particle Swarm Optimization-Elman Neural Network(PE)algorithm for the mobile human position prediction.Second,we propose an ADI-LSTM neural network prediction algorithm for pressure values of machines supports in underground mines.Among them,our proposed PE algorithm has the lowest average cumulative prediction error,and the trajectory fit rate is improved by 24.1%,13.9%and 8.7%compared with Kalman filtering,Elman and Kalman plus Elman algorithms,respectively.Meanwhile,compared with single-input ARIMA,RNN,LSTM,and GRU,the RMSE values of our proposed ADI-LSTM are reduced by 36.6%,52%,32%,and 13.7%,respectively;and the MAPE values are reduced by 0.0003%,0.9482%,1.1844%,and 0.3620%,respectively.

Monitoring and Environmental Assessment of Seawater in West Central Africa: Case of the Kribi Industrial and Urban Port Complex in Cameroon, Guinea Gulf

This study is the first of a series of a project on the development and implementation of environmental protection policies, before, during and after the construction of the Kribi Industrial and Urban Port Complex (KIPC). The results will equip the State and scientific structures concerned with the protection of people, water resources and the environment as a whole. This includes reference data on the state of marine pollution in the region dating from the end of realization of the first phase of KIPC known as Kribi Deep-Water Harbor (KDWH). Accordingly, the aim of this work is to assess the current state of KIPC and its surrounding by quantifying the preliminary parameters of suspended matter (SM);to analyze the physical and chemical parameters, chemical pollution indicators for anions and major cations and organic pollution indicators of four water samples taken from four different sites in the project area by filtration and weighing, pH meter, turbid meter, titration, colorimetry and titrimetric methods. The analysis of these samples and these parameters provides results which are slightly similar to international standards. This suggests that the environment remains relatively healthy. Hence, continuous management and monitoring of the parameters and pollution factors is strongly recommended.

Condition Monitoring of an Industrial Oil Pump Using a Learning Based Technique

This paper proposes an efficient learning based approach to detect the faults of an industrial oil pump.The proposed method uses the wavelet transform and genetic algorithm(GA)ensemble for an optimal feature extraction procedure.Optimal features,which are dominated through this method,can remarkably represent the mechanical faults in the damaged machine.For the aim of condition monitoring,we considered five common types of malfunctions such as casing distortion,cavitation,looseness,misalignment,and unbalanced mass that occur during the machine operation.The proposed technique can determine optimal wavelet parameters and suitable statistical functions to exploit excellent features via an appropriate distance criterion function.Moreover,our optimization algorithm chooses the most appropriate feature submatrix to improve the final accuracy in an iterative method.As a case study,the proposed algorithms are applied to experimental data gathered from an industrial heavy-duty oil pump installed in Arak Oil Refinery Company.The experimental results are very promising.

Industrial Temperature Monitoring System Design Based on ZigBee and Infrared Temperature Sensing

In the industrial field, long running of the equipment easily leads local temperature of the equipment to rise. This is a security risk. For this problem, we have designed a set of remote wireless temperature monitoring system. Based on ZigBee technology, we have a remote wireless networking temperature monitoring of a lot of equipment scattered in various locations of factories and enterprises. The system uses infrared temperature sensor TS118-3 gathering temperature information. After a signal conditioning circuit, we use a wireless RF single-chip CC2530 wirelessly transmitting the temperature of the measured target to the receiving node. The receiving node uploads the data to a computer by RS232.PC software displays real-time temperature information.

Method to deploy wireless relays in industrial wireless monitoring

In light of demands for wireless monitoring and the characteristics of wireless channel,a complete deployment method containing channel survey,path loss estimation,and gradient grade of wireless relay nodes is proposed.It can be proved by experiments that under the premise of meeting the requirements of real-time and redundant-topology,the total number of relay nodes could be minimized by using the proposed method.

A Cost-Efficient Environment Monitoring Robotic Vehicle for Smart Industries

Environmental monitoring is essential for accessing and avoiding the undesirable situations in industries along with ensuring the safety of workers.Moreover,inspecting and monitoring of environmental parameters by humans lead to various health concerns,which in turn brings to the requirement of monitoring the environment by robotics.In this paper,we have designed and implemented a cost-efficient robotic vehicle for the computation of various environmental parameters such as temperature,radiation,smoke,and pressure with the help of sensors.Furthermore,the robotic vehicle is designed in such a way that it can be dually controlled by using the remote control along with the distant computer.In addition,contrary to the existing researches,the GSM modules are used to achieve the two-way long distance communication between the robotic vehicle and the distant computer.On the distant computer,the above-mentioned environmental parameters can be monitored along with controlling the robotic vehicle with the help of Graphical User Interface(GUI).In order to fulfill the given tasks,we have proposed two algorithms implemented at the robotic vehicle and the distant computer respectively in this paper.The final results validate the proposed algorithms where the above-mentioned environmental parameters can be monitored along with the smooth-running operation of the robotic vehicle.

Communications in Industrial Monitor Software

Almost all industrial monitor software utilizes network communication. This paper mainly describes how the industrial monitor application selects the communication method. It also details some implementing techniques.

Smart and collaborative industrial IoT: A federated learning and data space approach

Industry 4.0 has become a reality by fusing the Industrial Internet of Things(IIoT)and Artificial Intelligence(AI),providing huge opportunities in the way manufacturing companies operate.However,the adoption of this paradigm shift,particularly in the field of smart factories and production,is still in its infancy,suffering from various issues,such as the lack of high-quality data,data with high-class imbalance,or poor diversity leading to inaccurate AI models.However,data is severely fragmented across different silos owned by several parties for a range of reasons,such as compliance and legal concerns,preventing discovery and insight-driven IIoT innovation.Notably,valuable and even vital information often remains unutilized as the rise and adoption of AI and IoT in parallel with the concerns and challenges associated with privacy and security.This adversely influences interand intra-organization collaborative use of IIoT data.To tackle these challenges,this article leverages emerging multi-party technologies,privacy-enhancing techniques(e.g.,Federated Learning),and AI approaches to present a holistic,decentralized architecture to form a foundation and cradle for a cross-company collaboration platform and a federated data space to tackle the creeping fragmented data landscape.Moreover,to evaluate the efficiency of the proposed reference model,a collaborative predictive diagnostics and maintenance case study is mapped to an edge-enabled IIoT architecture.Experimental results show the potential advantages of using the proposed approach for multi-party applications accelerating sovereign data sharing through Findable,Accessible,Interoperable,and Reusable(FAIR)principles.

Development and experience in the monitoring,assessment and control of environmentalpollutants in Hong Kong

The late start of environmental protection in Hong Kong was discussed in the light of problems encountered during the development of environmental protection legislation in Hong Kong for the past 20 years. The collaboration in monitoring and assessment of environmental pollutants between the University of Hong Kong and various governments were descrbed in parallel with the progress in environmental protection in Hong Kong. The developments of new analytical techniques for environmental monitoring and analysis is given and their application in environmental control described. The joint projects in assessment and control of environmental pollutants carried out in collaboration with local industries and other organizations within and without the university are given and discussed. The problems and possible solution facing Hong Kong in development control equipment for small scale industries are discussed and areas of development identified. The development and experience in the monitoring assessment and control of environmental pollutants in Hong Kong are summarized and areas of difficulties are illustrated.

Analytical Monitoring of the Chemicals Present in the Discharge Water Generated by the Surface Treatment Industry

Industrial discharge water, and especially that from the surface treatment industry (ST), contains non-negligible amounts of pollutants even though the legislation is fully respected. In spite of this, no detailed studies list the exact chemical composition of these effluents. The present study reports the results of analyses performed over a 6-month period involving 15 standard water parameters. Over 160 substances including 33 metals, 58 volatile organic compounds (VOCs), 16 polycyclic aromatic hydrocarbons (PAHs), 24 chlorophenols (CPs), 16 alkylphenols (APs), 5 chloroanilines (CAs) and 7 polychlorobiphenyls (PCBs) were monitored. The industrial effluents presented polycontamination involving metals, minerals and organics with a high degree of qualitative and quantitative variability. Of the 160 substances monitored, 46 were regularly found: 25 inorganics including 8 metals (Co, Cr, Cu, Fe, Ni, Pb, Sn, Zn) and 21 organics (4 PAHs, 10 VOCs, 4 CPs and 3 APs). Eighteen were systematically presented at quantifiable levels.

Industry 4.0 Application in Manufacturing for Real-Time Monitoring and Control

Modern manufacturing aims to reduce downtime and track process anomalies to make profitable business decisions.This ideology is strengthened by Industry 4.0,which aims to continuously monitor high-value manufacturing assets.This article builds upon the Industry 4.0 concept to improve the efficiency of manufacturing systems.The major contribution is a framework for continuous monitoring and feedback-based control in the friction stir welding(FSW)process.It consists of a CNC manufacturing machine,sensors,edge,cloud systems,and deep neural networks,all working cohesively in real time.The edge device,located near the FSW machine,consists of a neural network that receives sensory information and predicts weld quality in real time.It addresses time-critical manufacturing decisions.Cloud receives the sensory data if weld quality is poor,and a second neural network predicts the new set of welding parameters that are sent as feedback to the welding machine.Several experiments are conducted for training the neural networks.The framework successfully tracks process quality and improves the welding by controlling it in real time.The system enables faster monitoring and control achieved in less than 1 s.The framework is validated through several experiments.

基于数字孪生的工业机器人建模及监测方法

针对工业机器人数据不透明、监测存在死角等问题,通过对实体工业机器人分析和研究,构建数字孪生机器人监测系统。首先从几何、物理、逻辑3个维度完成虚拟机器人的建模;其次基于开放平台通信联合架构(open platform communications united architecture,OPC UA),实现机器人的数据采集和通信,设计用户显示界面,达到数据实时可视化的效果;最后,通过机器人仿真实验得出机器人末端执行器X、Y、Z坐标的相对位置误差分别为0.48%、0.32%、0.27%,通过数字孪生机器人监测同步实验验证机器人关节角度误差最大值为0.31°。实验结果表明:该方法能够实现数字孪生工业机器人的运动数据监测,减少工业机器人在生产过程中发生意外和故障的风险,为智能工业机器人的发展提供思路和方向。

工业大数据建模方法及生产线运行状态监控技术研究

随着我国科学技术水平的不断提升,大数据智能技术被广泛应用于工业生产中,有效推进了我国工业化建设的进程,与此同时,在工业生成过程中,数字化生产线的建设是提高工业生产效率,实现工业智能生产的关键环节。然而,在实际工业生产中,大部分车间智能化建设水平还比较低,监测工业制造流程的监控系统存在实时性较差以及准确性不足等问题。因此,文章基于大数据技术背景,利用智能信息通信技术,构建以大数据建模为基础的实时生产线运行状态监控系统,实现对生产流程的全面实时监测,加强工业生产的透明化,从而有效提高工业生产运营的管理水平。

网络安全态势感知标准在金融行业云场景下的应用实践

围绕金融行业云的网络安全运营需求,基于GB/T 42453—2023《信息安全技术网络安全态势感知通用技术要求》,在银联云上设计和构建了具有安全数据湖、流量解析、多源事件实时关联分析、智能AI安全分析等核心功能的网络安全态势感知平台。该平台能够对运营侧与租户侧面临的安全威胁进行持续监测预警和态势展示,有效满足了云上环境的资产发现、未知威胁检测、全量安全日志统一管理、安全事件处置、安全态势可视化等中小金融机构的安全需求,大幅提升其安全运营效果和效率。

基于运动模式划分的工业机器人健康监测方法

由于工业机器人存在多运动模式耦合的问题,传统的健康监测方案需要在每个关节处单独安装传感器,难以满足实际工业现场需求。本文以6关节工业机器人为研究对象,基于振动信号研究了多运动模式切换场景下工业机器人的健康监测方法。首先,通过跳变点算法实现机器人运动模式划分,获取不同运动模式对应的信号区间。其次,对不同运动模式的信号分别提取监测指标。最后,基于控制图法实现工业机器人不同关节的健康监测。在工业机器人退化实验数据中验证了本文所提方法,表明本文所提方法能够在仅使用两个振动传感器的条件下实现机器人6个关节的健康状态监测。本文所提运动模式划分算法在对大量历史退化数据进行分析时,所需运行时间更短、单次精度更高且重复性更好。本文提出的方法能够在使用少量传感器的条件下,有效避免运动模式耦合和采样信号的差异可能导致的监测结果误判,使得监测结果更加精准可靠,适用于实际工业现场的工业机器人健康状态监测。

数字化转型背景下金融风险监测与预警体系研究

当前,我国金融业数字化转型从多点突破进入深化和高质量发展的新阶段,需要协调包括管理部门、企业、个人在内的多元主体形成协同共治机制;针对数字化转型背景下产生的新型、复杂、潜在危害突出的金融风险问题,构建并提升金融风险监测与预警能力以切实保障金融安全,是金融业需要关注和亟待解决的核心课题。本文通过文献调研、理论分析等方式,分析了我国金融业数字化转型的进展、新型金融风险的内涵及特征,梳理了国内外主流的金融风险监测与预警技术进展,研判了金融风险监测与预警体系面临的风险表征识别、风险传导追踪、风险推理评估等方面的突出问题,提出了数字化转型背景下金融风险监测与预警体系的总体框架、创新研究方法、提升路径。研究发现,数字化转型背景下金融风险具有更新迭代更快、风险频次更高、隐蔽性更强等新特征,现有金融风险监测与预警技术在应对新型金融风险时存在诸多不足,面临着风险难表征、难追踪、难评估等诸多挑战。为此建议,加强行业协同、构建金融数据跨业共享标准,总结历史经验、形成金融风险知识表征范式与金融风险跨业传导机制,深化人工智能应用、构建金融风险监测与预警大模型,以提高我国金融风险防范水平、维护国家金融安全。

流程行业安全事故致因对应分析及监控策略

为降低流程行业安全事故发生的概率,综合运用系统思维、对应分析和案例分析相结合的方法识别了流程行业安全事故的关键致因因素,并提出了有针对性的安全管理监控方案。首先基于系统思维构建了包含人因、设备、环境和控制方法4个子系统的流程行业安全事故致因系统;然后运用对应分析方法确定了流程行业安全事故致因因素与事故等级、事故类型、事故发生行业之间的关联度;最后结合我国某石化公司重大爆炸着火事故案例,验证了该事故致因系统和对应分析结果的合理性。结果表明:监控过程中需要重点关注电火花、环境温度、原料控制、冲料现象、设备堵塞、可燃气体等致因因素,并采用视频监控和传感器等智能化技术手段对人因、设备和环境子系统实施监测与预警。该监控策略对事故预防具有参考价值,有助于推动流程行业生产安全智能监测的发展。

面向中小企业的工业机器人远程监控平台的设计

针对中小制造企业工业机器人设备在远程监测及故障诊断等方面存在的问题,从工业机器人设备的硬件接入、数据库搭建、设备运行信息数据采集及预警信息处理等方面进行研究,搭建面向中小制造企业的工业机器人远程监控云平台,实现企业和用户对于工业机器人等设备的远程动态监测及预警,提升企业对工业机器人设备的管理效率,为今后中小制造企业制造设备的平台化管理提供理论及技术应用方面的支撑。

基于模糊测试技术的电力工控系统漏洞挖掘算法

针对工控系统运行环境封闭带来的传统漏洞检测无法导出系统组件进行分析的问题,根据工控网络协议特征,结合测试用例变异因子,针对Modbus_TCP公开协议提出一种改进的电力工控系统漏洞测试挖掘方法。所提方法利用变异因子与工控协议特征依赖关系,改造协议测试用例。通过将测试用例的特征值和数据域的长度值因子及其数值的选择进行合并,并做归一化处理,进而简化协议测试过程中变异因子的执行次数。针对Modbus_TCP工控协议的模糊测试结果表明,改进后的Fuzzing测试表现出了更高的测试用例接收率和测试效率,测试样例的平均接收率至少提高50%。