Industrial Control Research Articles

Mirror-assisted 360° panoramic 3D measurement system based on rotary laser profilometer

Abstract The full surface digitization of real samples plays a crucial role in fields such as object recognition, industrial quality control, and reverse modeling. To achieve panoramic three-dimensional (3D) reconstruction of samples in complex scenes, we propose an advanced panoramic laser scanning system, consisting of a laser rotation scanning system and a pair of plane mirrors. With the assistance of mirrors, the projected line laser beam can illuminate the sample from full angles in a single scanning sequence, and the camera can capture the 3D information of the sample from several specific viewpoints. The scanning method of rotating allows us to achieve more efficient and larger-scale 3D scanning. After obtaining the sample’s 3D information from different viewpoints using the same calibration parameters, the sample’s full-surface 3D data in the global coordinate can be obtained using mirror reflections. We conducted precision and effectiveness tests on samples with different sizes, materials, shapes, and textures to demonstrate the ability of the proposed method to perform full-surface 3D reconstruction under different scenarios. The experimental results of accuracy evaluation demonstrate that the proposed system can achieve a measurement accuracy at the level of 100 μm. Additionally, the robust experimental results in different scenarios also indicate that the proposed panoramic laser scanning system is capable of accurately obtaining full-surface 3D data of various types of samples, providing a cost-effective, easy-to-use, efficient, high-resolution, and effective approach for achieving panoramic 3D reconstruction.

Enhancing trustworthiness among iot network nodes with ensemble deep learning-based cyber attack detection

A lot of machine learning methods and expert systems are used in network intrusion detection automation. When different industrial control systems merge with the Internet of Things (IoT) environment, they become vulnerable to cyber-attacks in critical infrastructure situations requiring communication technologies. Conventional machine learning techniques used for network anomaly detection are ineffective due to the substantial amount of network traffic within important Cyber-Physical Systems (CPSs). In this manuscript, Cyberattack Identification Through Ensemble Deep Learning in an IoT environment is proposed. Initially, the input network traffic data are taken from the IoT-23 dataset. Then the network traffic data are preprocessed using Z-Score normalization to reduce any irrelevant or erroneous data from the input dataset. Then the relevant features are selected using the Gorilla Troops Optimization (GTO) algorithm. Afterwards, the selected features are fed into the ensemble classification model based on Random Space (RS), Random Tree (RT), Extreme Gradient Boosting (XGBoost), and Graph Convolutional Neural Network (GCNN). Among the several ensembling techniques, GCNN can achieve better performance. Python is used to accomplish the suggested technique. The performance of the proposed GCNN method provides 12.09%, 4.34%, and 3.21% higher accuracy than the other models like RS, RT and XGBoost respectively.

One-Year Monitoring of Prevalence and Diversity of Dairy Propionic Acid Bacteria in Raw Milk by Means of Culture-Dependent and Culture-Independent Methods.

Even low levels of dairy propionic acid bacteria (dPAB) can cause cheese defects, resulting in severe economic losses for the producers of selected raw milk cheeses. Therefore, routine quality control of raw cheese milk for dPAB contamination is essential if propionic acid fermentation is undesired. Although knowledge of dPAB contamination of raw milk is important to understand cheese spoilage, long-term dPAB screening data are outdated, and studies taking into account different farm management parameters and their potential influence on dPAB levels are scarce. This study aims to provide insight into the dPAB levels of raw milk over time, to identify farm management factors that potentially influence dPAB levels, and to compare a cultural yeast extract lactate agar (YELA) and lithium glycerol agar (LGA) and a culture-independent method (qPCR) for dPAB quantification with respect to their applicability in routine quality control for the dairy industry. For this purpose, bulk tank milk from 25 dairy farms was screened for dPAB contamination over a one-year period. We were able to identify significant differences in the dPAB contamination levels in raw milk depending on selected farm-specific factors and observed relationships between the different types of milking systems and dPAB contamination levels in raw milk. When dPAB were quantified by cultivation on YELA, strong overgrowth of commensal microbiota impeded counting. Therefore, we conclude that quantification on LGA or by qPCR is preferable. Both methods, colony counting on LGA as well as quantification of dPAB using qPCR, have advantages for the application in (routine) quality control of raw milk, one being low-tech and inexpensive, the other being fast and highly specific, but the detection of (low level) dPAB contamination in raw milk remains a challenge.

Design and Development Considerations of a Cyber Physical Testbed for Operational Technology Research and Education.

Cyber-physical systems (CPS) are vital in automating complex tasks across various sectors, yet they face significant vulnerabilities due to the rising threats of cybersecurity attacks. The recent surge in cyber-attacks on critical infrastructure (CI) and industrial control systems (ICSs), with a 150% increase in 2022 affecting over 150 industrial operations, underscores the urgent need for advanced cybersecurity strategies and education. To meet this requirement, we develop a specialised cyber-physical testbed (CPT) tailored for transportation CI, featuring a simplified yet effective automated level-crossing system. This hybrid CPT serves as a cost-effective, high-fidelity, and safe platform to facilitate cybersecurity education and research. High-fidelity networking and low-cost development are achieved by emulating the essential ICS components using single-board computers (SBC) and open-source solutions. The physical implementation of an automated level-crossing visualised the tangible consequences on real-world systems while emphasising their potential impact. The meticulous selection of sensors enhances the CPT, allowing for the demonstration of analogue transduction attacks on this physical implementation. Incorporating wireless access points into the CPT facilitates multi-user engagement and an infrared remote control streamlines the reinitialization effort and time after an attack. The SBCs overwhelm as traffic surges to 12 Mbps, demonstrating the consequences of denial-of-service attacks. Overall, the design offers a cost-effective, open-source, and modular solution that is simple to maintain, provides ample challenges for users, and supports future expansion.

Concept of Multifactor Method and Non-Functional Requirements Solution to Increase Resilience through Functional Safety with Cybersecurity Analysis

In the process of designing safety systems, an integrated approach in safety and cybersecurity analysis is necessary. The paper describes a new technique of increasing resilience through integrated analysis of functional safety and cybersecurity. It is a modeling methodology based on the combination of the multifactor method utilizing modified risk graphs, used previously for Safety Integrity Level (SIL) assessment, and the Non-Functional Requirements (NFR) approach. The NFR approach, based on the analysis of graphical representation of conceptual and physical components of the system, contributes a technique to include cybersecurity through the Softgoal Interdependency Graph. The assessment methodology is outlined in detail and applied to a case study involving an industrial control system. The analysis turns out to be effective in both aspects: confirming the findings of the multifactor approach based on modified risk graphs and complementing the traditional analysis to increase resilience in discovering and mitigating security vulnerabilities for SIL assessment by the use of NFR.

Regional comparison study of Epimedium koreanum using UHPLC-QTOF/MS-based metabolomics approach

The untargeted metabolomics-based molecular networking approach combined with multivariate analysis, proves to be an effective strategy for distinguishing raw materials in herbal medicine according to specific criteria. It exhibits the correlations between chemical constituents and the geographical habitats of plants, providing a valuable tool for ensuring quality control in mass production within the industry. In this study, we conducted a comprehensive investigation of the chemical compositions of Epimedium koreanum Nakai and performed comparative analyses on four extracts collected from distinct regions in South Korea using untargeted metabolomics tools. Through the comprehensive use of UPLC-QTOF/MS analysis and advanced statistical techniques, we elucidated the chemical composition, leading to the identification of key chemical markers. Additionally, the molecular networking analysis revealed distinct clusters of flavonoids and phenolic acids, highlighting the influence of regional factors on the metabolite profiles. These findings offer a promising avenue for enhancing quality control and traceability in the herbal medicine industry, underscoring the important role of geographical variation in the chemical profiles of herbal products.

Efficient Cyberattack Detection Methods in Industrial Control Systems.

The article deals with the issue of detecting cyberattacks on control algorithms running in a real Programmable Logic Controller (PLC) and controlling a real laboratory control plant. The vulnerability of the widely used Proportional-Integral-Derivative (PID) controller is investigated. Four effective, easy-to-implement, and relatively robust methods for detecting attacks on the control signal, output variable, and parameters of the PID controller are researched. The first method verifies whether the value of the control signal sent to the control plant in the previous step is the actual value generated by the controller. The second method relies on detecting sudden, unusual changes in output variables, taking into account the inertial nature of dynamic plants. In the third method, a copy of the controller parameters is used to detect an attack on the controller's parameters implemented in the PLC. The fourth method uses the golden run in attack detection.

Design and Implementation of IPsec VPN IoT Gateway System in National Secret Algorithm

With the development of Internet of Things technology, the security threats faced by the industrial control field are increasing, and strengthening the security protection capabilities of intelligent systems on IoT highways is becoming increasingly important. IPSec VPN tunneling technology can achieve identity authentication and encrypted data transmission, and is an important means to achieve secure data transmission in intelligent systems on Expressway intelligent tunnel system. The commonly used IPSec VPN gateway uses a traditional Linux protocol stack-based approach for data capture, which requires multiple data copies and context switching, resulting in low efficiency of IPSec services. In addition, the commonly used IPSec VPN security gateway is implemented on the basis of the open-source IPSec framework, using internationally recognized algorithms for encryption and decryption, which poses security risks. This article is based on the IPSec protocol, and studies the high-speed network packet capture framework PFRING technology, the fusion technology of national secret algorithm and IPSec protocol. It designs and implements an IPSec VPN IoT security gateway based on national secret algorithm. After experimental verification, the IPSec VPN gateway system constructed in this article has complete functions and better performance than the common open-source IPSec frameworks OpenSwan and strongSwan, and can meet the application requirements of IoT data encryption transmission.

Guide to developing case-based attack scenarios and establishing defense strategies for cybersecurity exercise in ICS environment

Critical infrastructure mainly performs its role through an industrial control system (ICS). Organizations conduct cyber exercises between red and blue teams, focusing on offense and defense. Practical exercises require explicit attack scenarios and corresponding defense strategies. However, systematic guides for deriving cyberattack scenarios or defense strategies still need to be improved. This paper proposes a guide for establishing realistic attack scenarios and defense strategies for cybersecurity exercises in ICS environments. Attack scenario generation is divided into four steps: generating attack references, deriving attack sequences, mapping threat information, and mapping vulnerable implementation patterns. Deriving a defensive strategy consists of two steps parallel to developing an attack scenario: deriving containment and eradication. The methodology we propose guides exercise planning based on a knowledge base, thereby assisting exercise planners in generating various scenarios and deriving clear defense strategies. We showed that a clear exercise plan could be established through a case study.

Multi-component quantitative analysis of LIBS using adaptively optimized multi-branch CNN

Multi-component quantitative analysis of laser-induced breakdown spectroscopy (LIBS) is very important for industrial process control, food safety and space exploration. Due to the strong background interference and complex matrix effect, traditional quantitative analysis methods are difficult to realize accurate multi-component regression. Deep learning has the advantages of automatically extracting full-spectrum features and strong nonlinear modeling capabilities, which provides new opportunities for advancing the progress of LIBS quantitative analysis. However, due to the large difference of spectral line intensity and spectral cross-interference, existing deep learning models have limited performance for simultaneous multi-component regression. To this end, a novel adaptively optimized multi-branch convolution neural network (AOMB-CNN) framework is proposed for multi-component quantitative analysis of LIBS. Multi-branch structure is adopted to ensure the accuracy of multi-component content simultaneously. Dynamic weighted loss function and Bayesian optimization algorithm are used to adaptively optimize the deep learning model. The results demonstrate that the AOMB-CNN can effectively improve the performance of multi-component regression on 2 LIBS datasets compared with the traditional quantitative analysis methods. The root mean square error (RMSE) of 8 components of the ChemCam test set are 2.4465, 0.3486, 1.2481, 1.3909, 0.9010, 0.8892, 0.6700, and 0.5498 respectively, and the RMSE of 4 components of the steel test sets are 1.9787, 0.3455, 0.6025 and 1.6631 respectively. The proposed method provides an end-to-end, adaptively optimized, full-spectrum deep learning solution for LIBS multi-component content measurement without human intervention.

Electrifying Fruit Juice: Integrating Applied Electroanalytical Chemistry into the Undergraduate Curriculum.

Electroanalytical chemistry has been advanced through portable devices, providing methods and sensors for the detection of analytes with high sensitivity and accuracy. This subfield of electrochemistry has the potential to be utilized in industry and analytical quality control, in general. This results in an increasing demand for trained personnel, capable of operating benchtop and portable electroanalytical equipment. Although electrochemical techniques are routinely taught in theoretical undergraduate courses, they need to be more often incorporated into experimental didactic activities. Herein, we describe the application of an effective, hands-on, and low-maintenance experiment that can enhance the learning experience of electroanalytical methods and their use in industrial quality control settings. This activity is based on the detection of ascorbic acid (vitamin C) by employing cyclic voltammetry at unmodified glassy carbon electrodes (GCE) in real juice samples. This didactic experiment teaches students about the theoretical concepts of cyclic voltammetry, three-electrode cell setup, chemical reversibility, data treatment, and quantitative analysis. This teaching approach was conducted in a second-year analytical chemistry course and was easily adapted to social distancing measures imposed by the COVID-19 pandemic.

Design and experimental evaluation of a data‐driven PID controller using cerebellar memory

AbstractIn industrial process control, the proportional–integral–derivative (PID) control scheme is well‐recognized and widely utilized. However, due to the distinctive characteristics of real systems, their control design primarily aims at achieving optimal production performance, constrained by uncertainty and variations. This paper initially discusses a database‐driven PID (DD‐PID) control scheme that was previously proposed. This scheme combines the DD‐PID with the cerebellar model articulation control to minimise computational and memory requirements for industrial application. Subsequently, a hydraulic system is introduced, detailing its characteristics and control necessities. Furthermore, both the DD‐PID and the proposed cerebellar model articulation control memory‐based DD‐PID control schemes are implemented and evaluated through experimental examples on a hydraulic system. Lastly, as a practical validation of the theoretical approach, a quantitative assessment compares the two methods, discussing the practicality and efficacy of the proposed scheme in reducing computation and memory consumption.

SCADA World: An Exploration of the Diversity in Power Grid Networks

Despite a growing interest in understanding the industrial control networks that monitor and control our critical infrastructures (such as the power grid), to date, SCADA networks have been analyzed in isolation from each other. They have been treated as monolithic networks without taking into consideration their differences. In this paper, we analyze real-world data from different parts of a power grid (generation, transmission, distribution, and end-consumer) and show that these industrial networks exhibit a variety of unique behaviors and configurations that have not been documented before. To the best of our knowledge, our study is the first to tackle the analysis of power grid networks at this level. Our results help us dispel several misconceptions proposed by previous work, and we also provide new insights into the differences and types of SCADA networks.

The Effect of Internal Audit, Internal Control and Audit Committee on Fraud Prevention (Case Study at Bank Mandiri Jakarta Tanjung Priok Area)

Fraud has often occurred in the corporate environment. Fraud is usually based on personal, group or business interests. The economic crisis has encouraged corporate fraud, especially due to the weak implementation of internal control and corporate governance in the banking industry. In an effort to prevent fraud, companies can consistently empower the role of the company's internal auditors who can trigger the implementation of management risk control, internal control systems, and audit committees which have an important role in various aspects of the organization including fraud prevention. This study aims to examine the influence of the role of internal audit, internal control, and the audit committee on fraud prevention. The population in this study used 7 of the 25 Bank Mandiri Branch Offices in the Tanjung Priok Enggano Area, with a total of 85 respondents. The data collection method used a questionnaire by giving a score value according to the Likert scale measurement system . This type of research used quantitative research with a case study method. The sampling technique used purposive sampling. The data analysis method used is multiple linear regression with the SPSS version 25 application. The results show that internal auditing, internal control, and audit committees have a significant effect on fraud prevention.

Terahertz time-domain spectroscopy imaging combined with convolutional long short-term memory neural network for In situ moisture reduction prediction of particle foods during pulsed fluidized bed drying

Fluidized bed drying (FBD), as a novel dehydration method, has been widely applied in drying various heat-sensitive high-value food products, in which the pulsed fluidized bed drying (PFBD) technique addresses the ineffective fluidized limitation of the traditional FBD when dealing with cohesive products. However, achieving in situ real-time monitoring of dynamic product moisture content (MC) change during both FBD and PFBD without sampling is still challenging. This paper presents an innovative integration of a whisk-broom terahertz time-domain spectroscopy (THz-TDS) imaging system with a fluidized bed dryer, facilitating in situ, real-time monitoring of MC reduction of products without process pausing for sampling. The reliability, and robustness of the established system and prediction models were tested by drying peppercorn and sweetcorn under various drying settings, including different temperatures, flow patterns, and durations. Implementing the convolutional neural networks (CNN) and long short-term memory (LSTM) for correlating terahertz time-domain image data with reference datasets in this research provided reliable MC reduction prediction results for the mixture dataset from peppercorn and sweetcorn drying. The successful application of this combined sensing and analytical method presents a promising approach for enhancing industrial processing control and expands the utility of THz-TDS in the agriculture and food industrial fields.

Pulsed processing by cold plasma, applied to industrial emission control.

A promising pollution control technology is cold plasma driven chemical processing. The plasma is a pulsed electric gas discharge inside a near atmospheric-pressure-temperature reactor. The system is energized by a continuous stream of very short high-voltage pulses. The exhaust gas to be treated flows through the reactor. The methods applied involve the development of robust cold plasma systems, industrial applications and measuring technologies. Tests of the systems were performed at many industrial sites and involved control of airborne VOC (volatile organic compound) and odor. Electrical, chemical and odor measuring data were collected with state-of-the-art methods. To explain the test data an approximate solution of global reaction kinetics of pulsed plasma chemistry was developed. It involves the Lambert function and, for convenience, a simple approximation of it. The latter shows that the amount of removal, in good approximation, is a function of a single variable. This variable is electric plasma power divided by gas flow divided by input concentration. In the results sections we show that in some cases up to 99% of volatile pollution can be removed at an acceptable energy requirement. In the final sections we look into future efficiency enhancements by implementation of (sub)nanosecond pulsed plasma and solid state high-voltage technology and by integration with catalyst technology.

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens with known, unchangeable defect properties, which are usually complicated to fabricate. It consist of a shape memory polymer foil with temperature-dependent Young’s modulus and ultrasound attenuation. This open a possibility to generate a reconfigurable defect by projecting a heating laser in the form of a short line on the specimen surface. Ultrasound is generated by a laser pulse at one fixed position and detected by a laser vibrometer at another fixed position for 64 different defect positions and 3 different configurations of the specimen. The obtained diversified datasets are used to optimize the neural network architecture for the interpretation of ultrasound signals. We study the performance of the model in cases of reduced and dissimilar training datasets. In our first study, we classify the specimen configurations with the defect position being the disturbing parameter. The model shows high performance on a dataset of signals obtained at all the defect positions, even if trained on a completely different dataset containing signals obtained at only few defect positions. In our second study, we perform precise defect localization. The model becomes robust to the changes in the specimen configuration when a reduced dataset, containing signals obtained at two different specimen configurations, is used for the training process. This work highlights the potential of the demonstrated machine learning algorithm for industrial quality control. High-volume products (simulated by a reconfigurable specimen in our work) can be rapidly tested on the production line using this single-point and contact-free laser ultrasonic method.

Sample-efficient reinforcement learning with knowledge-embedded hybrid model for optimal control of mining industry

The froth flotation process is a cost-effective and widely employed method for mineral separation. A core challenge is to maximize mineral recovery while maintaining a specified minimum concentrate grade. This requires precise control of aeration and slurry level and overcoming disturbances. The rapid advancements in reinforcement learning present a promising approach for controlling froth flotation. However, reinforcement learning suffers from sample inefficiency, which significantly hampers its application to real-world problems. In this research, we proposed an innovative sample-efficient model-based reinforcement learning algorithm to enhance flotation performance by directly leveraging the dynamics of the slurry phase. Our proposed algorithm involves the construction of a hybrid model, comprising a physical model and a data residual model, which effectively learns the underlying dynamics of the flotation process during the interaction between the reinforcement learning agent and process. The physical model integrates domain knowledge into the hybrid model to expedite the training process and enhance the interpretability and generalizability of the hybrid model. To capture uncertainty and address unmodeled aspects of the physical model, we employ an ensemble data residual model. Actor and critic are augmented with fuzzy representation modules based on fuzzy logic inference to improve the learning capacity of the networks and the multi-head critic is applied to reduce overestimation biases and enhance training stability. Case studies demonstrate that, compared to the baseline algorithm, our proposed algorithm requires merely 86.8% of the samples employed by conventional model-based reinforcement learning algorithm and improves the long-term return by at least 12.0%, underscoring the role of domain knowledge in facilitating efficient reinforcement learning.

Visually exploring canonical correlation patterns of high-dimensional industrial control datasets based on multi-sensor fusion

Visually exploring canonical correlation patterns of high-dimensional industrial control datasets based on multi-sensor fusion

Investigating PID Controller Effectiveness: An Extensive Analysis and Wide-ranging Uses

This study offers an in-depth investigation of the performance of proportional-integral-derivative (PID) controllers, providing an extensive evaluation and analyzing a wide range of applications. PID controllers, essential feedback mechanisms in various domains, are recognized as foundational instruments in control systems. This study evaluates PID controllers in in scenarios such as DC motor speed regulation, power system stability, Automatic Voltage Regulator (AVR) performance optimization, time delay processes, load frequency management, DC drive control, systematic process stability, and their developments in industrial control. The purpose of the study is to highlight the adaptability and effectiveness of PID controllers in contemporary control systems, including how artificial intelligence (AI) can be integrated into PID controller tuning.