A Novel Medium Access Control Protocol for Real-Time Wireless Communications in Industrial Automation

In this paper, a novel Medium Access Control (MAC) protocol for industrial Wireless Local Area Networks (WLANs) is proposed and studied. The main challenge in industry automation systems is the ultra-low network latency with a target upper bound in the order of 1 ms while maintaining high network reliability and availability. The novelty of the proposed wireless MAC protocol resides in its similar latency performance as its counterpart in wired industrial LAN. First, the functional design of the MAC protocol is introduced. Then its performance results gained from hardware implementation (SystemC and VHDL) on an FPGA platform are presented. Finally, a real-time communication module which achieves the ultra-low latency required in industrial automation is described.

Industrial Automation Information Analogy for Smart Grid Security

Industrial automation or assembly automation is a strictly monitored environment,in which changes occur at a good speed.There are many types of entities in the focusing environment,and the data generated by these devices is huge.In addition,because the robustness is achieved by sensing redundant data,the data becomes larger.The data generating device,whether it is a sensing device or a physical device,streams the data to a higher-level deception device for calculation,so that it can be driven and configured according to the updated conditions.With the emergence of the Industry 4.0 concept that includes a variety of automation technologies,various data is generated through numerous devices.Therefore,the data generated for industrial automation requires unique Information Architecture(IA).IA should be able to satisfy hard real-time constraints to spontaneously change the environment and the instantaneous configuration of all participants.To understand its applicability,we used an example smart grid analogy.The smart grid system needs an IA to fulfill the communication requirements to report the hard real-time changes in the power immediately following the system.In addition,in a smart grid system,it needs to report changes on either side of the system,i.e.,consumers and suppliers configure and reconfigure the system according to the changes.In this article,we propose an analogy of a physical phenomenon.A point charge is used as a data generating device,the streamline of electric flux is used as a data flow,and the charge distribution on a closed surface is used as a configuration.Finally,the intensity changes are used in the physical process,e.g.,the smart grid.This analogy is explained by metaphors,and the structural mapping framework is used for its theoretical proof.The proposed analogy provides a theoretical basis for the development of such information architectures that can represent data flows,definition changes(deterministic and non-deterministic),events,and instantaneous configuration definitions of entities in the system.The proposed analogy provides a mechanism to perform calculations during communication, using a simpleconcept on the closed surface to integrate two-layer cyber-physical systems(computation, communication, and physical process). The proposed analogyis a good candidate for implementation in smart grid security.

IT Asset Management of Industrial Automation Systems

1. Introduction The installation and administration of large heterogeneous IT infrastructures, for enterprises as well industrial automation systems, are becoming more and more complex and time consuming. The growing number of interconnections between networks, the development of new intelligent IT devices, and increasingly sophisticated computer hard-

IEC and China Industrial Automation

A long with the development of global economy and science technology, IEC international standards as the important technical trade measure are taking on more and more prominence in world trade. Under consideration of international competition, many countries joint in IEC works in process actively and make effort on consistent between national stan dards and IEC standards, in order to obtain the advantage position within the international competition.……

Data Driven CAN Node Reliability Assessment for Manufacturing System

The reliability of the Controller Area Network（CAN） is critical to the performance and safety of the system. However, direct bus-off time assessment tools are lacking in practice due to inaccessibility of the node information and the complexity of the node interactions upon errors. In order to measure the mean time to bus-off（MTTB） of all the nodes, a novel data driven node bus-off time assessment method for CAN network is proposed by directly using network error information. First, the corresponding network error event sequence for each node is constructed using multiple-layer network error information. Then, the generalized zero inflated Poisson process（GZIP） model is established for each node based on the error event sequence. Finally, the stochastic model is constructed to predict the MTTB of the node. The accelerated case studies with different error injection rates are conducted on a laboratory network to demonstrate the proposed method, where the network errors are generated by a computer controlled error injection system. Experiment results show that the MTTB of nodes predicted by the proposed method agree well with observations in the case studies. The proposed data driven node time to bus-off assessment method for CAN networks can successfully predict the MTTB of nodes by directly using network error event data.

AI Based Traffic Flow Prediction Model for Connected and Autonomous Electric Vehicles

There is a paradigm shift happening in automotive industry towards electric vehicles as environment and sustainability issues gainedmomentum in the recent years among potential users.Connected and Autonomous Electric Vehicle(CAEV)technologies are fascinating the automakers and inducing them to manufacture connected autonomous vehicles with self-driving features such as autopilot and self-parking.Therefore,Traffic Flow Prediction(TFP)is identified as a major issue in CAEV technologies which needs to be addressed with the help of Deep Learning(DL)techniques.In this view,the current research paper presents an artificial intelligence-based parallel autoencoder for TFP,abbreviated as AIPAE-TFP model in CAEV.The presented model involves two major processes namely,feature engineering and TFP.In feature engineering process,there are multiple stages involved such as feature construction,feature selection,and feature extraction.In addition to the above,a Support Vector Data Description(SVDD)model is also used in the filtration of anomaly points and smoothen the raw data.Finally,AIPAE model is applied to determine the predictive values of traffic flow.In order to illustrate the proficiency of the model’s predictive outcomes,a set of simulations was performed and the results were investigated under distinct aspects.The experimentation outcomes verified the effectual performance of the proposed AIPAE-TFP model over other methods.

Real-time performance of periodic data transmission in EPA industrial Ethernet

To evaluate and improve the real-time performance of Ethernet for plant automation（EPA） industrial Ethernet,the real-time performance of EPA periodic data transmission was theoretically and experimentally studied.By analyzing information transmission regularity and EPA deterministic scheduling mechanism,periodic messages were categorized as different modes according to their entering-queue time.The scheduling characteristics and delivery time of each mode and their interacting relations were studied,during which the models of real-time performance of periodic information transmission in EPA system were established.On this basis,an experimental platform is developed to test the delivery time of periodic messages transmission in EPA system.According to the analysis and the experiment,the main factors that limit the real-time performance of EPA periodic data transmission and the improvement methods were proposed.

Identifying and Tracing Single Products During Their Entire Life Cycle Through Networked Manufacturing System

This study covers the problem that most products become less competitive especially in the decline stages of their life cycle as most companies do not put adequate emphasis on using networked manufacturing systems in the entire life cycle of a single product. The study employed a non-experimental approach to collect data. The research paper relied on secondary data for further analysis. The secondary sources used in this paper have been referenced progressively in the entire paper. The paper found that most companies are often faced with the challenge of coping with quality management in a product life cycle. Also, it found that networked manufacturing systems have provided a new paradigm for real-time monitoring and control at various life stages. The paper is divided as following parts： section 1 is about the background and problem statement. Section 2 comes through literature review including theoretical ＆ empirical review. Section 3 explains the procedures and methods that were used in carrying out the study. It explains how data collection was carried out and how data analysis was performed. Section 4 is about the results the paper found. Section 5 is a discussion of the results presented.

Automated Angle Detection for Industrial Production Lines Using Combined Image Processing Techniques

Angle detection is a crucial aspect of industrial automation,ensuring precise alignment and orientation ofcomponents in manufacturing processes.Despite the widespread application of computer vision in industrialsettings,angle detection remains an underexplored domain,with limited integration into production lines.Thispaper addresses the need for automated angle detection in industrial environments by presenting a methodologythat eliminates training time and higher computation cost on Graphics Processing Unit(GPU)from machinelearning in computer vision(e.g.,Convolutional Neural Networks(CNN)).Our approach leverages advanced imageprocessing techniques and a strategic combination of algorithms,including contour selection,circle regression,polar warp transformation,and outlier detection,to provide an adaptive solution for angle detection.By configuringthe algorithm with a diverse dataset and evaluating its performance across various objects,we demonstrate itsefficacy in achieving reliable results,with an average error of only 0.5 degrees.Notably,this error margin is 3.274times lower than the acceptable threshold.Our study highlights the importance of accurate angle detection inindustrial settings and showcases the reliability of our algorithm in accurately determining angles,thus contributingto improved manufacturing processes.

Bioreactor control systems in the biopharmaceutical industry:a critical perspective

Industrial-scale bioprocessing underpins much of the production of pharmaceuticals,nutraceuticals,food,and beverage processing industries of the modern world.The proftability of these processes increasingly leverages the economies of scale and scope that are critically dependent on the product yields,titers,and productivity.Most of the processes are controlled using classical control approaches and represent over 90%of the industrial controls used in bioprocessing industries.However,with the advances in the production processes,especially in the biopharmaceutical and nutraceutical industries,monitoring and control of bioprocesses such as fermentations with GMO organisms,and downstream processing has become increasingly complex and the inadequacies of the classical and some of the modern control systems techniques is becoming apparent.Therefore,with increasing research complexity,nonlinearity,and digitization in process,there has been a critical need for advanced process control that is more efective,and easier process intensifcation and product yield(both by quality and quantity)can be achieved.In this review,industrial aspects of a process and automation along with various commercial control strategies have been extensively discussed to give an insight into the future prospects of industrial development and possible new strategies for process control and automation with a special focus on the biopharmaceutical industry.

Research on target recognition and mobile job control of humanoid robot based on KM34Z256 for industrial applications

Purpose-Humanoid robot has similar shape and action characteristics as humans,and it can complete some basic tasks instead of humans without changing the human environment,which makes humanoid robot become the best structure and help form for robot to provide services for human beings.Design/methodology/approach-The mobile operation control of humanoid robot is generated by the walking movement of humanoid robot’s feet,and the robot’s hand and arm complete grasping and other operations together.Findings-On the basis of humanoid robot,the integrated system of software and hardware based on the KM34Z256 humanoid robot is described first,and a series of kinematics discussion on its mobile operation is carried out.Originality/value-The research based on this project shows that the target recognition and positioning method is not only accurate and of high energy but also can realize the mobile operation of humanoid robot.