Industrial Control Research Articles

An Immersive Digital Twin Applied to a Manufacturing Execution System for the Monitoring and Control of Industry 4.0 Processes

An Immersive Digital Twin Applied to a Manufacturing Execution System for the Monitoring and Control of Industry 4.0 Processes

A novel generative adversarial network-based fuzzing cases generation method for industrial control system protocols

With the interconnection and networking of industrial control systems (ICS), security vulnerabilities in ICS protocols have become a major source of threats to these systems. In order to discover these vulnerabilities and maintain ICS security, generative adversarial network-based fuzz testing (Fuzzing) has been introduced as a popular method to discover vulnerabilities in industrial control system protocols (ICSPs). However, the current approach based on generative adversarial network fuzz testing suffers from problems in terms of compatibility and repetitive generation of test examples, and low acceptance rate. To address these issues, this paper proposes a fuzzing approach based on the synergy of Wasserstein Generative Intelligent Networks (WGAN) and Variation Autoencoder (VAE). In addition, we design an intelligent fuzz framework, WGGFuzz, to efficiently generate a large number of test cases while ensuring the accuracy of the testing results for efficiently generating a large number of test cases in a short period of time. The framework, which does not incorporate ICSPs applicable to a wide range of, shows through experiments that WGGFuzz can be able to achieve 91% in terms of TCRR, 1.41 in terms of ATE, and better performance in terms of DGD.

Integrated simulation and control environment for the development and safe start-up of cable driven parallel robots

ABSTRACT This article presents a new environment designed to facilitate the conceptualization and automation of CDPRs (Cable Driven Parallel Robots). Utilizing a platform for robotic simulation, an industrial controller, and a data gateway to communicate both systems, it is possible to emulate CDPR motion in early design, development, and start-up phases. Thus, this simulation environment allows to test how the robot will behave in advance in order to obtain a safer and more reliable first operation with the real robot. An spatially under-constrained CDPR with four cables was used to validate the capabilities of the proposed integrated environment, comparing its performance against an industry-standard hardware prototype. Both the simulation model and the real prototype performed the same trajectory in order to assess a time delay and position errors between them. Evaluation results revealed that the simulation accurately replicated the performance of its industrial counterpart, with a peak divergence of 0.27% in the position of the robot under various speed scenarios. This occurs even when accounting for latency arising from data exchange connecting the industrial control unit to the simulation. The results have shown that the environment is suitable for trajectory control of new CDPR architectures under different dynamic conditions.

Dynamic vulnerability severity calculator for industrial control systems

Dynamic vulnerability severity calculator for industrial control systems

Enhancing Industrial Process Control: Integrating Intelligent Digital Twin Technology with Proportional-Integral-Derivative Regulators

This paper explores the integration of intelligent digital twin technology with PID regulators in industrial process control utilizing smart meter data. It presents a novel approach involving the creation of mathematical models to simulate real-time system behavior, thereby enhancing the PID control loop. The focus is on the development of specialized IT infrastructure to support this integration, which includes data acquisition, processing, and control optimization. This integration aims to not only improve control system efficiency but also introduce a robust predictive maintenance framework, offering significant benefits across a wide range of industrial applications.

Design Pattern and Challenges of Federated Learning with Applications in Industrial Control System

Design Pattern and Challenges of Federated Learning with Applications in Industrial Control System

Improved extended empirical wavelet transform for accurate multivariate oscillation detection and characterisation in plant-wide industrial control loops

The conventional extended empirical wavelet transform (EEWT) proposed recently is intended to decompose multivariate signals with clear peaks in power spectra without considering the cases where the signals contain high noise levels. Even when dealing with signals with distinct peaks, the EEWT method can still encounter challenges in properly decomposing the signals. However, plant-wide data from industrial control loops, including controllers’ outputs, process variables, and manipulated variables, are commonly corrupted by high levels of noise, which can be introduced at various stages of data acquisition, transmission, and processing within the control system. To address these limitations and ensure the applicability of the EEWT to real-world industrial data with diverse and challenging characteristics, this paper presents an improved version called the improved extended empirical wavelet transform (IEEWT). The IEEWT incorporates noise-reduced power spectra and detrended fluctuation analysis techniques to enhance the decomposition. The proposed method demonstrates accurate multivariate data decomposition for both simulated and real data sets, surpassing the limitations associated with the conventional EEWT.

Assessing the Impact of PM2.5-Bound Arsenic on Cardiovascular Risk among Workers in a Non-ferrous Metal Smelting Area: Insights from Chemical Speciation and Bioavailability.

Inhalation of fine particulate matter PM2.5-bound arsenic (PM2.5-As) may cause significant cardiovascular damage, due to its high concentration, long transmission range, and good absorption efficiency in organisms. However, both the contribution and the effect of the arsenic exposure pathway, with PM2.5 as the medium, on cardiovascular system damage in nonferrous smelting sites remain to be studied. In this work, a one-year site sample collection and analysis work showed that the annual concentration of PM2.5-As reached 0.74 μg/m3, which was 120 times the national standard. The predominant species in the PM2.5 samples were As (V) and As (III). A panel study among workers revealed that PM2.5-As exposure dominantly contributed to human absorption of As. After exposure of mice to PM2.5-As for 8 weeks, the accumulation of As in the high exposure group reached equilibrium, and its bioavailability was 24.5%. A series of animal experiments revealed that PM2.5-As exposure induced cardiac injury and dysfunction at the environmental relevant concentration and speciation. By integrating environmental and animal exposure assessments, more accurate health risk assessment models exposed to PM2.5-As were established for metal smelting areas. Therefore, our research provides an important scientific basis for relevant departments to formulate industry supervision, prevention and control policies.

Enhancing Data Preservation and Security in Industrial Control Systems through Integrated IOTA Implementation

Within the domain of industrial control systems, safeguarding data integrity stands as a pivotal endeavor, especially in light of the burgeoning menace posed by malicious tampering and potential data loss. Traditional data storage paradigms, tethered to physical hard disks, are fraught with inherent susceptibilities, underscoring the pressing need for the deployment of resilient preservation frameworks. This study delves into the transformative potential offered by distributed ledger technology (DLT), with a specific focus on IOTA, within the expansive landscape of the Internet of Things (IoT). Through a meticulous examination of the intricacies inherent to data transmission protocols, we present a novel paradigm aimed at fortifying data security. Our approach advocates for the strategic placement of IOTA nodes on lower-level devices, thereby streamlining the transmission pathway and curtailing vulnerabilities. This concerted effort ensures the seamless preservation of data confidentiality and integrity from inception to storage, bolstering trust in the convergence of IoT and DLT technologies. By embracing proactive measures, organizations can navigate the labyrinthine terrain of data management, effectively mitigate risks, and cultivate an environment conducive to innovation and progress.

Industrial Human Safety Detection System

Industrial workplaces can be hazardous environments where the safety and well-being of workers are paramount. To mitigate risks and ensure the safety of employees, a wide range of industrial human safety devices have been developed and implemented. This Research explores various safety devices designed to protect and enhance the safety of workers in industrial settings. The study examines devices such as personal protective equipment (PPE), machine guarding devices, fall protection gear, gas detection devices, and more. Through a comprehensive review of the current state of industrial safety technology, we assess the effectiveness, advantages, and limitations of these safety devices. We also discuss emerging trends in industrial safety technology, including the integration of IoT and AI. The findings presented in this Research will aid in understanding the critical role that these devices play in safeguarding industrial workers, and highlight the need for ongoing innovation in the field of industrial safety.. The real-time monitoring of human presence allows for prompt intervention and a more secure working environment . Furthermore, the automated safety measures based on human detection contribute to overall safety protocols and reduce the risk of injuries or mishaps. The successful implementation of this device demonstrates the efficiency and reliability of human detection using computer vision techniques. The device's ability to provide real-time monitoring and ensure immediate intervention significantly enhances workplace safety. It establishes a solid foundation for future integrating it with existing industrial control devices.

A real-time network based anomaly detection in industrial control systems

Data manipulation attacks targeting network traffic of SCADA systems may compromise the reliability of an Industrial Control system (ICS). This can mislead the control center about the real-time operating conditions of the ICS and can alter commands sent to the field equipment. Deep Learning techniques appear as a suitable solution for detecting such complicated attacks. This paper proposes a Network based Anomaly Detection System (NADS) to detect data manipulation attacks with a focus on Modbus/TCP-based SCADA systems. The proposed NADS is a sequence to sequence auto encoder which uses the long short term memory units with embedding layer, teacher forcing technique and attention mechanism. The model has been trained and tested using the SWaT dataset, which corresponds to a scaled-down water treatment plant. The model detected 23 of 36 attacks and outperformed two other existing NADS with an improvement of 0.22 for simple attacks and obtained a recall value of 0.86 on attack 36 compared to the other NADS which obtained 0.74.

Synthesis of resilient fallback control system under cyber-attacks via supervisory control

Industrial control systems (ICS) require system design and operation under cyber-attacks. This study aims to design a fallback control system that can switch from normal control to fallback control and verify its superiority. The target system is a factory automation (FA) system consisting of a normal programmable logic controller (PLC) and a fallback PLC. In this system, we design a fallback logic that takes over control in the case of cyber-attacks. The design of this logic requires a system model that manages smooth state transitions between normal control and fallback control in an integrated manner under cyber-attacks. In response, we model a control program in the framework of discrete event systems (DESs) and apply supervisory control to derive a supervisor model that can manage the system in an integrated manner. To ensure the controllability of the FA system during cyber-attacks, we design a control specification that includes a detection function to enable rapid switching of the control state. As a result, we generate the fallback logic from the supervisor model with guaranteed controllability under cyber-attacks and implement it in a fallback PLC to verify the effectiveness of the proposed logic.

An online intrusion detection method for industrial control systems based on extended belief rule base

An online intrusion detection method for industrial control systems based on extended belief rule base

Industrial environmental control of polycyclic aromatic hydrocarbons in organic fuel combustion product

   RELEVANCE of this study lies in the computational justification of the choice of the installation location of a multipoint sampling probe in the measuring section of the flue of boiler plants of energy enterprises and thermal power plants that have a significant negative impact of emissions on the environment, in order to increase the reliability and representativeness ofinstrumental measurements during industrial environmental control (PEC) of polluting emissions (PE).   PURPOSE. Improving the environmental safety of thermal power plants in the current conditions of the introduction of state principles of technological regulation of emissions is associated with the establishment of technological emission indicators for each source of atmospheric pollution at energy enterprises with a significant negative impact on the environment. In this regard, energy enterprises should carry out round-the-clock monitoring of PE, which requires reliable instrumental control of the content of marker substances in the combustion products of power boilers by determining a representative sampling point on the flue or chimney of an energy boiler installation.   METHODS. The paper uses methods of computer simulation of gas dynamics to determine the local fields of concentration of PE and flow velocity and assess their unevenness in the flue of an energy boiler. RESULTS. Using the example of an operating energy boiler plant of a thermal power plant, a control section of the flue was determined to ensure industrial environmental control of marker substances in the combustion products of organic fuels. The average values of the concentration of PE and the flow rate of combustion products of local fields in the calculated sections and their values on the longitudinal axis of the boiler flue are determined, and their uneven distribution is estimated.   CONCLUSION. The obtained research results can be used at energy enterprises and thermal power plants to justify the choice of a control representative measuring section for conducting a PEC, and to develop an environmental efficiency improvement program (EEIP).

Experimental and modeling investigation on dynamic response of sticky control valves

The dynamic response of control valves directly affects the safe and efficient operation of industrial control loops. Valve stiction is a common and persistent issue that causes oscillations in control loops. The stiction behavior of valves has received widespread attention from researchers worldwide in the past two decades. The developed valve stiction models have been widely applied in the detection, quantification, and compensation research of sticky control valves. However, how to more accurately characterize stiction behavior still requires efforts. Most data-driven models do not consider the effects of dynamic response on the stiction behavior. In this paper, the inconsistency of the representative stiction models is discussed during the unidirectional motion of the valve stem, and potential improvements are revealed. An experimental device for valve stiction has been designed. This device can replicate valve stiction caused by tight packing, and measure valve position and friction through smart positioner and force sensor. The second-order dynamic system for the sticky valve is constructed by the physical model, and the response time of the valve is calculated and verified combined with the experiment. The effects of sampling interval on stiction behavior are discussed. On this basis, an improved valve stiction model considering the dynamic response of the valve is proposed, which supplements the situation of stiction during the unidirectional motion of the valve stem. The proposed model can be applied in control systems with fixed sampling intervals that involve sticky valves. It can also be extended to control systems with variable sampling intervals. This work contributes to evaluating the performance of control systems with sticky valves and providing reference value for the detection, quantification, and compensation of valve stiction.

IOT Based DC Motor Speed and Direction Control

This paper introduces an innovative Internet of Things (IoT) based system designed to control the speed and direction of a DC motor using the Blynk mobile application. The system integrates an OLED display (SSD1306), an ESP8266 microcontroller, a 300 RPM DC motor, and an L293D motor driver. With this system, users can remotely manage the speed and direction of the DC motor through the Blynk app, accessible on smartphones or tablets. Real-time feedback on the motor's speed and direction is provided via the OLED display, enhancing user interaction and experience. The ESP8266 microcontroller acts as the core control unit, interfacing with the Blynk app via Wi-Fi to receive user commands and adjust the motor's operation accordingly. The bidirectional control feature of the L293D motor driver enables precise manipulation of the motor's speed and direction. By leveraging IoT technology and the Blynk app, users gain the flexibility to control the motor from any location with internet connectivity, offering convenience and accessibility. Additionally, the OLED display enhances the user interface, enabling intuitive monitoring and adjustment of motor parameters. In conclusion, this system offers a versatile and user-friendly solution for remotely managing DC motor speed and direction, with potential applications spanning across home automation, robotics, and industrial control systems.

Effective adsorption of fluorescent congo red azo dye from aqueous solution by green synthesized nanosphere ZnO/CuO composite using propolis as bee byproduct extract.

The indirect dumping of massive volumes of toxic dyes into water has seriously affected the ecosystem. Owing to the many applications of the designed nanomaterials in the manufacturing process, there is a lot of research interest in synthesizing nanomaterials using green processes. In this research, the byproduct of bee was employed to synthesize nanoparticles (NPs) of ZnO, CuO, and biosynthesized ZnO/CuO (BZC) nanocomposite via utilizing a green and simple approach. To validate the effective fabrication of BZC nanocomposite, various characterization measurements were applied. FTIR analysis identified the functional groups in charge of producing nanoparticles and nanocomposites. Moreover, the existence of ZnO and CuO XRD peaks suggests that the nanocomposites were successfully biosynthesized. The high-resolution XPS spectrum of the BZC nanocomposite's Zn2p3, Cu2p3, and O1s were observed. Our findings indicate the successful engineering of the prepared nanomaterials and BZC nanocomposite. Our findings indicate the successful engineering of the prepared nanomaterials and BZC nanocomposite. For Congo red (CR) fluorescent stain azo dye elimination in water, all adsorption parameters were examined at room temperature. Moreover, the adsorption experiments revealed the removal capacity for uptake CR dye using BZC nanocomposite (90.14mgg-1). Our results show that the BZC nanocomposite exhibited high removal capability for the adsorption of CR dye. The nanosphere adsorbent offered a simple, low-cost, and green approach for water purification and industrial wastewater control.

Comparative Analysis of Anomaly Detection Approaches in Firewall Logs: Integrating Light-Weight Synthesis of Security Logs and Artificially Generated Attack Detection.

Detecting anomalies in large networks is a major challenge. Nowadays, many studies rely on machine learning techniques to solve this problem. However, much of this research depends on synthetic or limited datasets and tends to use specialized machine learning methods to achieve good detection results. This study focuses on analyzing firewall logs from a large industrial control network and presents a novel method for generating anomalies that simulate real attacker actions within the network without the need for a dedicated testbed or installed security controls. To demonstrate that the proposed method is feasible and that the constructed logs behave as one would expect real-world logs to behave, different supervised and unsupervised learning models were compared using different feature subsets, feature construction methods, scaling methods, and aggregation levels. The experimental results show that unsupervised learning methods have difficulty in detecting the injected anomalies, suggesting that they can be seamlessly integrated into existing firewall logs. Conversely, the use of supervised learning methods showed significantly better performance compared to unsupervised approaches and a better suitability for use in real systems.

Mitigating Missing Rate and Early Cyberattack Discrimination Using Optimal Statistical Approach with Machine Learning Techniques in a Smart Grid

In the Industry 4.0 era of smart grids, the real-world problem of blackouts and cascading failures due to cyberattacks is a significant concern and highly challenging because the existing Intrusion Detection System (IDS) falls behind in handling missing rates, response times, and detection accuracy. Addressing this problem with an early attack detection mechanism with a reduced missing rate and decreased response time is critical. The development of an Intelligent IDS is vital to the mission-critical infrastructure of a smart grid to prevent physical sabotage and processing downtime. This paper aims to develop a robust Anomaly-based IDS using a statistical approach with a machine learning classifier to discriminate cyberattacks from natural faults and man-made events to avoid blackouts and cascading failures. The novel mechanism of a statistical approach with a machine learning (SAML) classifier based on Neighborhood Component Analysis, ExtraTrees, and AdaBoost for feature extraction, bagging, and boosting, respectively, is proposed with optimal hyperparameter tuning for the early discrimination of cyberattacks from natural faults and man-made events. The proposed model is tested using the publicly available Industrial Control Systems Cyber Attack Power System (Triple Class) dataset with a three-bus/two-line transmission system from Mississippi State University and Oak Ridge National Laboratory. Furthermore, the proposed model is evaluated for scalability and generalization using the publicly accessible IEEE 14-bus and 57-bus system datasets of False Data Injection (FDI) attacks. The test results achieved higher detection accuracy, lower missing rates, decreased false alarm rates, and reduced response time compared to the existing approaches.

Design and multilevel reconstruction method of intelligent power industry control system based on digital twins

Design and multilevel reconstruction method of intelligent power industry control system based on digital twins