工业控制网络的发展

阐述了工业控制网络及其特点,简要介绍了目前国际上已经应用的几种主要的控制网络,最后对控制网络的发展趋势进行了展望。     

基于Industrial IT的水泥生产过程控制系统设计

分析了水泥生产过程控制的特点,采用国际先进的ABB Industrial IT系统为巨龙水泥厂1#生产线设计一套过程控制系统,构建基于现场总线网络和以太网络的整体控制体系。全面实现电气和仪表控制的自动化、数字化、网络化和信息化。并对该系统的组成、工作原理、硬件设计、控制软件进行了论述。经过半年多的实际运行效果显示,系统功能完善,可靠性高,取得了显著的经济效益。表明了水泥生产过程综合自动化的发展趋势。     

Modelling Industrial Wastewater Ozonation In Bubble Contactors. 1. Rate Coefficient Determination

A study of the ozonation of distillery and tomato wastewaters was carried out in a small bubble contactor in order to obtain kinetic data for scaling-up. Thus, several parameters, such as chemical oxygen demand (COD), 254 nm absorbance (A₂₅₄) and organic carbon content (OC), were followed during ozonation at different experimental conditions.

For distillery wastewaters all parameters investigated have the highest decreases during the first minutes of ozonation, A₂₅₄ showing the highest disappearance rates. Thus, during the first fifteen minutes of ozonation an important decrease of the 254 nm absorbance (? 75%) was observed. At further reaction times values of all parameters studied decrease slowly, eventually reaching a plateau value. During approximately the first two hours of reaction, dissolved ozone was never found, which suggested that fast or moderate gas-liquid reactions took place in the wastewaters.     

QoE enhancement of the industrial metaverse based on Mixed Reality application optimization

As a comprehensive integration of many new-generation information technologies, the metaverse has become a research hotspot that has attracted much attention. As a part of the metaverse, the industrial metaverse is expected to break through the constraints of space and time and promote high-quality industrial development. The industrial metaverse is human-centric, so its quality of experience (QoE) is a key topic. As one of the enabling technologies of the industrial metaverse, Mixed Reality (MR) can seamlessly integrate virtual information with the physical world and is widely regarded as an important window to the industrial metaverse. In close integration with other enabling technologies, industrial MR applications can be seen as a path toward the realization of the industrial metaverse; thus, the optimization of industrial MR applications can effectively achieve the QoE enhancement of the industrial metaverse. Based on the analysis of existing research and the characteristics of industrial scenarios, consistency, authenticity, smoothness, and comfort are identified as the factors that influence the user experience (UX) of industrial MR applications. Specific optimization methods for industrial MR applications are proposed to improve the UX with regard to these four factors. To verify the effectiveness of the proposed methods, a QoE evaluation model of the industrial metaverse based on the fuzzy analytic hierarchy process (FAHP) is established. Moreover, an industrial metaverse prototype for longwall mining that incorporates the proposed methods is developed and its QoE is evaluated. The results show that the proposed optimization methods for industrial MR applications significantly enhance the QoE in the industrial metaverse, and can provide better services for users in industrial systems, thus better serving these systems.     

An effective MBSE approach for constructing industrial robot digital twin system

Recently, the rapid development of digital twin (DT) technology has been regarded significant in Cyber-physical systems (CPS) promotion. Scholars are focusing on the theoretical architecture and implementing applications, in order to establish a high-fidelity, dynamic, and full-lifecycle DT model and achieve a deep fusion of real and virtual. As a typical complex system with multi-disciplines, multi-physics, and multi-domain characteristics, industrial robot (IR) involves various processes and elements from the two other levels of the system: components and production lines. Their complex relationships lead to a huge challenge to build a comprehensive DT model. Current researchers usually concentrates on single-layer services because of limited construction methodology, which results in enormous isolated models, and leads to low reusable system blocks, finite scalability, and high costs of design, adjustment, upgrade, and maintenance. To address these issues, a standardized methodology and a hierarchical, modular, and generic architecture are proposed to depict comprehensive and variable industrial robot digital twin (IRDT). Firstly, the ontology information model is presented by analyzing variable factors systematically. Then, model-based system engineering (MBSE) based methodology is introduced, including construction process and variants management. After modeling process of three levels (problem domain, solution main, and implementation domain) and four viewpoints (requirement, structure, behavior, and parameter), a generic architecture of IRDT is constructed and a feature-based variants management method is described. Besides, a six-axis IRDTS is implemented to illustrate the mapping of logical architecture and physical system as a multi-level elements and processes representation example. And the steps of numerical evaluations consist of system delay and derivation. Finally, results show the effectiveness and the potential of the proposed theoretical methodology for constructing IRDTS and other industrial applications.     

Anomaly Detection for Industrial Internet of Things Cyberattacks

The evolution of the Internet of Things (IoT) has empowered modern industries with the capability to implement large-scale IoT ecosystems, such as the Industrial Internet of Things (IIoT). The IIoT is vulnerable to a diverse range of cyberattacks that can be exploited by intruders and cause substantial reputational and financial harm to organizations. To preserve the confidentiality, integrity, and availability of IIoT networks, an anomaly-based intrusion detection system (IDS) can be used to provide secure, reliable, and efficient IIoT ecosystems. In this paper, we propose an anomaly-based IDS for IIoT networks as an effective security solution to efficiently and effectively overcome several IIoT cyberattacks. The proposed anomaly-based IDS is divided into three phases: pre-processing, feature selection, and classification. In the pre-processing phase, data cleaning and normalization are performed. In the feature selection phase, the candidates’ feature vectors are computed using two feature reduction techniques, minimum redundancy maximum relevance and neighborhood components analysis. For the final step, the modeling phase, the following classifiers are used to perform the classification: support vector machine, decision tree, k-nearest neighbors, and linear discriminant analysis. The proposed work uses a new data-driven IIoT data set called X-IIoTID. The experimental evaluation demonstrates our proposed model achieved a high accuracy rate of 99.58%, a sensitivity rate of 99.59%, a specificity rate of 99.58%, and a low false positive rate of 0.4%.     

Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

With the increased advancements of smart industries, cybersecurity has become a vital growth factor in the success of industrial transformation. The Industrial Internet of Things (IIoT) or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether. In industry 4.0, powerful Intrusion Detection Systems (IDS) play a significant role in ensuring network security. Though various intrusion detection techniques have been developed so far, it is challenging to protect the intricate data of networks. This is because conventional Machine Learning (ML) approaches are inadequate and insufficient to address the demands of dynamic IIoT networks. Further, the existing Deep Learning (DL) can be employed to identify anonymous intrusions. Therefore, the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection (HGSODL-ID) model for the IIoT environment. The presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful format. The HGSO algorithm is employed for Feature Selection (HGSO-FS) to reduce the curse of dimensionality. Moreover, Sparrow Search Optimization (SSO) is utilized with a Graph Convolutional Network (GCN) to classify and identify intrusions in the network. Finally, the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN model. The proposed HGSODL-ID model was experimentally validated using a benchmark dataset, and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.     

典型工业粉尘地区复合绝缘子污秽成分及其闪络特性

近年来,盐城德隆镍业厂区附近数次发生输电线路外绝缘放电、污闪现象,且采用复合绝缘子后仍不能得到有效抑制,而临近的化工区却没无该情况发生。因此,本文对盐城电网辖区内镍厂、化工厂绝缘子进行污秽取样,开展了污秽化学成分分析测试,包括阴阳离子含量、不溶物元素组成、主要化合物成分等;继而基于成分分析结果,开展了混合污秽下的污闪特性试验,得到了不同污染源典型污秽成分下复合绝缘子的闪络特性。研究结果表明：化工厂和镍厂附近绝缘子污秽成分有一定的差异,体现在镍厂污秽成分含硝酸盐较多,而化工厂附近含氯盐较多;对于一大两小型复合绝缘子,其在镍厂典型污秽下的闪络梯度要比化工厂低5%-10%;对于重工业粉尘地区的外绝缘配置,建议采用一大一小型绝缘子来提高电气强度。     

汽车数字供应链SaaS化新管理模式探究与实践

随着现代技术的发展,汽车产业孕育着新一轮升级。目前,汽车企业间的竞争逐步加大,已经逐步转化为供应链的竞争。而传统供应链生产管理仍然面临着市场高质量、高交期的巨大挑战,无法满足汽车企业对其生产效率的需求。因此,新的汽车供应链管理模式应运而生。研究了汽车数字供应链软件即服务(SaaS)化新管理模式,并基于SaaS平台实现探索供应链数字化管理的新生态模式。以某汽车公司为例,进行供应链SaaS生态平台实践研究,以数字化制造重塑供应链流程,解决供应链企业间数据协同,实现汽车全产业链互联互通、结构性降本增效,打造高效协作、互利共赢的新型行业生态模式。采用SaaS平台实现供应链生产管理后,其供应链管理数字化延伸,生产线开动率提升了1%,协同效率大幅提升。