Industrial Control Research Articles

LIBS as a fast and reliable alternative to µXRF and SEM–EDX for quantitative analysis of aluminium alloy particles in technical cleanliness analysis

The characterization of metal alloy (particle) characterization is a vital tool in today’s high–tech applications for quality and process control in industry, especially for applications in technical cleanliness analysis (TCA). Here, metal alloy fragments down to sizes of 200 µm need to be quantitatively characterized as accurate as possible. Laser-induced breakdown spectroscopy (LIBS) offers several advantages due to its easy use, short measurements times, and high sensitivity. To enable the application of LIBS in this context, the method must yield adequately accurate results. Therefore, in this study we assess the performance of LIBS by comparison with well–established methods – scanning electron microscopy coupled with energy-dispersive X‑ray spectroscopy (SEM-EDX) and µ‑X‑ray fluorescence (µXRF). We analyse particulate certified reference materials consecutively with non–destructive and destructive methods on the quantitative composition regarding the key elements necessary for distinguishing aluminium alloys: Si, Zn, Mn, Mg, Cu and Fe. Sample inhomogeneity and the effect of sampling location and area is shown via microstructure analysis and combined with an assessment of the analysis volume for each used method. By evaluating the data statistically and by comparing quantitative results achieved with different methods, we can postulate a high agreement between LIBS and the studied reference methods, providing a basis for the use of LIBS in industrial analysis of metal alloy particles.

Artificial intelligence-driven tool for spectral analysis: identifying pesticide contamination in bees from reflectance profiling

Pesticide poisoning constantly threatens bees as they forage for resources in pesticide-treated crops. This poisoning requires thorough investigation to identify its causes, underscoring the importance of reliable pesticide detection methods for bee monitoring. Infrared spectroscopy provides reflectance data across hundreds of spectral bands (hyperspectral reflectance), presumably enabling the efficient classification of pesticide contamination in bee carcasses using artificial intelligence (AI) models, such as machine learning. In this study, bee contamination by commercial formulations of three insecticides—dimethoate (organophosphate), fipronil (phenylpyrazole), and imidacloprid (neonicotinoid)—as well as glyphosate, the most widely used herbicide globally, was detected using machine learning models. These models classified the hyperspectral reflectance profiles of the body surfaces of contaminated bees. The best-performing model, the linear discriminant analysis, achieved 98% accuracy in discriminating contamination across species Apis mellifera, Melipona mondury, and Partamona helleri, with prediction speeds of 0.27seconds. Our pioneering study introduced an effective method for discerning multiple classes of bees contaminated with pesticides using hyperspectral reflectance. An AI-driven spectral data analysis tool (https://github.com/bernardesrodrigoc/MACSS) was developed for the purpose of identifying and characterizing new samples through their spectral characteristics. This platform aids efforts to monitor and conserve bee populations and holds potential importance in environmental monitoring, agricultural research, and industrial quality control.

Deep one-class classification model assisted by radius constraint for anomaly detection of industrial control systems

Anomaly detection of industrial control systems (ICS) based on sensor data analytic is of utmost importance because ICS may suffer from various attacks leading to anomaly behaviors and even equipment failures. As an emerging deep learning technique, deep support vector data description (DeSVDD) has been successfully applied to ICS anomaly detection. Its advantage lies in only requiring normal data to train one-class classifier, which is suitable for actual industrial scenarios with extremely data imbalance of abundant normal data and scare abnormal data. However, this also results in its shortcoming of ignoring the valuable classification information hidden in the abnormal data. In order to overcome this issue, this paper proposes an improved DeSVDD method, called radius constraint DeSVDD (RC-DeSVDD). The proposed model constructs an anomaly detection model framework of abnormal data assisted DeSVDD, where a radius constraint is designed by considering the difference between normal and abnormal data. Further, considering the limited amount of abnormal data, a bi-directional generative adversarial network (BiGAN) is introduced to generate abnormal data. Experimental results on three benchmark datasets demonstrate the superiority of the proposed RC-DeSVDD anomaly detection method.

Factorial inputs and outputs: A virtual plant for industrial control training and operations

The field of manufacturing and production is rapidly changing because of the fourth Industrial Revolution or Industry 4.0 and bridging theory and practice is a major requirement in industrial control and automation training, operations and virtual simulation is one such way to help bridge this kind of gap. One of such simulation program that has gained popularity among companies recently is Factory inputs and outputs (Factory I/O). This paper presents an example of a used case study of FACTORY I/O. The tank scene in the Factory I/O was used for level control and the control algorithm was implemented in the TIA portal using Siemens S7 1500 PLC integrated with the factory I/O to carry out the control of the liquid level in the tank by controlling the fill valve on the level control rig. The S7-PLCSIM TIA portal V15 communicated with the PLC in the Factory I/O drivers through an IP address. Therefore, with virtual simulation system, it is possible to test controller and control configuration before applying it to a physical system, with a methodology matching the fourth industrial revolution in a simulation environment in which theory and practice is bridged for real-time simulations.

Minimising cybersecurity risk exposures in industrial control system environments: a techno-human vulnerability analysis approach

ABSTRACT Organisations operating IoT-enabled industrial control systems (ICSs) are concerned about growing cybersecurity risks and impacts to their systems. Cyber-attacks on ICSs demonstrate that technology alone is neither a problem nor a solution to the growing cybersecurity issues affecting these systems. As socio-technical systems, ICSs encompass the functions and interactions of social and technological system elements to enable and/or sustain industrial processes. Thus, a more effective cybersecurity risk management process needs to consider human and technology factors, especially for high-value industrial process targets. Combining critical reviews and gap analysis of existing vulnerability assessment methods with conceptual modelling, a Vulnerability Analysis Critical Impact Point Process (VACIP) methodology is proposed which considers both human and technological vulnerabilities within a cyber-physical system environment to inform an improved insight about attack criticality and impacts. VACIP is validated using a simulated industrial mini testbed; showing that it can enable practicable support for security vulnerability discovery, impact criticality analysis, weak link identification, and prioritised controls. Its novelty is demonstrated in its combination of technology and human vulnerability evaluations in the minimisation of system security exposures. It provides a useful guide for adopting effective cybersecurity risk assessment and exposure reduction strategies.

Adaptive Industrial Control Systems via IEC 61499 and Runtime Enforcement

This work envisions industrial control systems that can reliably adapt to requirements. We rely on the international standard IEC 61499 to achieve this goal. The standard allows downtimeless system evolution such that an application can be modified at runtime to satisfy the requirements. However, an IEC 61499 application consisting of multiple Function Blocks (FBs) can be modified in many different ways, such as inserting or deleting FBs, creating new FBs with their respective internal behaviours and adjusting the connections between FBs. These changes require considerable effort and cost, and there is no guarantee to satisfy the requirements. This article applies runtime enforcement techniques for supporting adaptive IEC 61499 applications. This set of techniques can modify the runtime behaviour of a system according to specific requirements. Our approach begins with specifying the requirements as a state machine-based notation called contract automaton. This automaton is then used to synthesise an enforcer as an FB. Finally, the new FB is integrated into the application to execute according to the requirements. A tool support is developed to automate the approach. Experiments were performed to evaluate the performance of enforcers by measuring the execution time of several applications before and after the integration of enforcers.

Hybrid-DC: A Hybrid Framework Using ResNet-50 and Vision Transformer for Steel Surface Defect Classification in the Rolling Process

This study introduces Hybrid-DC, a hybrid deep-learning model integrating ResNet-50 and Vision Transformer (ViT) for high-accuracy steel surface defect classification. Hybrid-DC leverages ResNet-50 for efficient feature extraction at both low and high levels and utilizes ViT’s global context learning to enhance classification precision. A unique hybrid attention layer and an attention fusion mechanism enable Hybrid-DC to adapt to the complex, variable patterns typical of steel surface defects. Experimental evaluations demonstrate that Hybrid-DC achieves substantial accuracy improvements and significantly reduced loss compared to traditional models like MobileNetV2 and ResNet, with a validation accuracy reaching 0.9944. The results suggest that this model, characterized by rapid convergence and stable learning, can be applied for real-time quality control in steel manufacturing and other high-precision industries, enhancing automated defect detection efficiency.

What Do We Know About CapsicumVolatilome?

The Capsicum genus includes several cultivated species that release complex blends of volatile organic compounds (VOCs) associated with their unique aroma. These VOCs are essential info-chemicals in ecological interactions. In this review, we describe how the volatilomic profiling naturally varies based on specific plant organs and genotypes as well as how non-beneficial organisms affect VOCs biosynthesis and accumulation in pepper plants. Also, we show evidence about VOCs variation under the pressure of different abiotic factors such as water stress, soil type and nutrient availability. The contribution of specific metabolic pathways and gene expression related to the biosynthesis of particular VOCs is addressed. We highlighted the utility of VOCs as chemical markers for quality control in the food industry, breeding programs to generate resistant plants and to improve aroma innovation. Herein we present a database containing 2734 VOCs, revealing 113 as the basic core of the volatilome from five Capsicum species.

Network-Based Intrusion Detection for Industrial and Robotics Systems: A Comprehensive Survey

In the face of rapidly evolving cyber threats, network-based intrusion detection systems (NIDS) have become critical to the security of industrial and robotic systems. This survey explores the specialized requirements, advancements, and challenges unique to deploying NIDS within these environments, where traditional intrusion detection systems (IDS) often fall short. This paper discusses NIDS methodologies, including machine learning, deep learning, and hybrid systems, which aim to improve detection accuracy, adaptability, and real-time response. Additionally, this paper addresses the complexity of industrial settings, limitations in current datasets, and the cybersecurity needs of cyber–physical Systems (CPS) and Industrial Control Systems (ICS). The survey provides a comprehensive overview of modern approaches and their suitability for industrial applications by reviewing relevant datasets, emerging technologies, and sector-specific challenges. This underscores the importance of innovative solutions, such as federated learning, blockchain, and digital twins, to enhance the security and resilience of NIDS in safeguarding industrial and robotic systems.

A Reconfigurable Architecture for Industrial Control Systems: Overview and Challenges

The closed architecture and stand-alone operation model of traditional industrial control systems limit their ability to leverage ubiquitous infrastructure resources for more flexible and intelligent development. This restriction hinders their ability to rapidly, economically, and sustainably respond to mass customization demands. Existing proposals for open and networked architectures have failed to break the vicious cycle of closed architectures and stand-alone operation models because they do not address the core issue: the tight coupling among the control, infrastructure, and actuator domains. This paper proposes a reconfigurable architecture that decouples these domains, structuring the control system across three planes: control, infrastructure, and actuator. The computer numerical control (CNC) system serves as a primary example to illustrate this reconfigurable architecture. After reviewing open and networked architectures and discussing the characteristics of this reconfigurable architecture, this paper identifies three key challenges: deterministic control functionality, the decoupling of control modules from infrastructures, and the management of control modules, infrastructures, and actuators. Each challenge is examined in detail, and potential solutions are proposed based on emerging technologies.

A Novel Defect Segmentation Model via Integrating Enhanced U-Net with Convolutional Block Attention Mechanisms

Abstract. Defect segmentation in industrial quality control is pivotal for maintaining high product surface standards, but it remains a challenge due to complex defect patterns and varying backgrounds. Traditional segmentation methods often struggle with sensitivity and specificity, leading to either missed defects or false positives. In order to address the above issues this paper presents an advanced defect segmentation model that integrates a Convolutional Block Attention Module (CBAM) with an enhanced U-Net architecture. Our method leverages the spatial and channel-wise attention mechanisms to focus more accurately on subtle defects and remove background interference, enhancing the discriminative power of the feature maps generated by the U-Net. Experimental results on SD-saliency-900 industrial datasets demonstrate significant improvements in segmentation accuracy and robustness compared to existing state-of-the-art methods, confirming the effectiveness of integrating attention mechanisms in deep learning models for complex defect segmentation tasks. Specifically, our model achieved a mean Intersection over Union (mIoU) of 0.87, which is an increase of 5% over the traditional U-Net model and a 4% improvement compared to the DeepLabv3+ model. These results indicate a substantial enhancement in defect detection performance, validating the superiority of our approach.

A Security Situation Prediction Model for Industrial Control Network Based on Explainable Belief Rule Base

Industrial Control Systems (ICSs) are vital components of industrial production, and their security posture significantly impacts operational safety. Given that ICSs frequently interact with external networks, cyberattacks can disrupt system symmetry, thereby affecting industrial processes. This paper aims to predict the network security posture of ICSs to ensure system symmetry. A prediction model for the network security posture of ICSs was established utilizing Evidence Reasoning (ER) and Explainable Belief Rule Base (BRB-e) technologies. Initially, an evaluation framework for the ICS architecture was constructed, integrating data from various layers using ER. The development of the BRB prediction model requires input from domain experts to set initial parameters; however, the subjective nature of these settings may reduce prediction accuracy. To address this issue, an ICS network security posture prediction model based on the Explainable Belief Rule Base (BRB-e) was proposed. The modeling criteria for explainability were defined based on the characteristics of the ICS network, followed by the design of the inference process for the BRB-e prediction model to enhance accuracy and precision. Additionally, a parameter optimization method for the explainable BRB-e prediction model is presented using a constrained Projection Equilibrium Optimization (P-EO) algorithm. Experiments utilizing industrial datasets were conducted to validate the reliability and effectiveness of the prediction model. Comparative analyses indicated that the BRB-e model demonstrates distinct advantages in both prediction accuracy and explainability when compared to other algorithms.

Using machine learning to detect network intrusions in industrial control systems: a survey

Using machine learning to detect network intrusions in industrial control systems: a survey

A Security Posture Assessment of Industrial Control Systems Based on Evidential Reasoning and Belief Rule Base

With the rapid advancements in information technology and industrialization, the sustainability of industrial production has garnered significant attention. Industrial control systems (ICS), which encompass various facets of industrial production, are deeply integrated with the Internet, resulting in enhanced efficiency and quality. However, this integration also introduces challenges to the continuous operation of industrial processes. This paper presents a novel security assessment model for ICS, which is based on evidence-based reasoning and a library of belief rules. The model consolidates diverse information within ICS, enhancing the accuracy of assessments while addressing challenges such as uncertainty in ICS data. The proposed model employs evidential reasoning (ER) to fuse various influencing factors and derive security assessment values. Subsequently, a belief rule base is used to construct an assessment framework, grounded in expert-defined initial parameters. To mitigate the potential unreliability of expert knowledge, the chaotic mapping adaptive whale optimization algorithm is incorporated to enhance the model’s accuracy in assessing the security posture of industrial control networks. Finally, the model’s effectiveness in security assessment was validated through experimental results. Comparative analysis with other assessment models demonstrates that the proposed model exhibits superior performance in ICS security assessment.

Improving the Dynamics of an Electrical Drive Using a Modified Controller Structure Accompanied by Delayed Inputs

This paper presents the operation of a modified speed controller with a standard PI/PID structure that includes the preprocessing of the controller’s input signal, focusing on the past behavior of control errors. The modification involves adding a delay line, with the outputs of the individual line segments summed with a weighting method, as detailed in the paper. One of the significant advantages of this method is its use of a standard industrial controller structure, which makes it highly practical and easily implementable in existing systems. By relying on well-established control frameworks, this approach reduces the need for specialized hardware or complex modifications, allowing for smoother integration and lower implementation costs. The delay-based signal shaping shows excellent properties for the electric drive system powered by a hard-switching PWM converter. The set of weighted delays acts as a filter whose parameters are chosen using the quality function to test different configurations for optimal performance. When tested in a speed control system for a Permanent Magnet Synchronous Motor, the modifications improved the control quality index, indicating better performance and efficiency. Importantly, the system allows for reducing or eliminating the gain in the differentiating part of the controller, which decreases motor current chattering and noise. This paper includes an experimental verification of the proposed solution in a laboratory setting under semi-industrial conditions.

Exploring the effects of RNNs and deep learning frameworks on real‐time, lightweight, adaptive time series anomaly detection

SummaryReal‐time, lightweight, adaptive time series anomaly detection is increasingly critical in cybersecurity, industrial control, finance, healthcare, and many other domains due to its capability to promptly process time series and detect anomalies without requiring extensive computation resources. While numerous anomaly detection approaches have emerged recently, they generally employ a single type of recurrent neural network (RNN) and are implemented using a single type of deep learning framework. The impacts of using various RNN types across different deep learning frameworks on the performance of these approaches remain unclear due to a lack of comprehensive evaluations. In this article, we aim to investigate the impact of different RNN variants and deep learning frameworks on real‐time, lightweight, and adaptive time series anomaly detection. We reviewed several state‐of‐the‐art anomaly detection approaches and implemented a representative approach using several RNN variants supported by three popular deep learning frameworks. A thorough evaluation was conducted to analyze the detection accuracy, time efficiency, and resource consumption of each implementation using four real‐world, open‐source time series datasets. The results show that RNN variants and deep learning frameworks have a significant impact. Therefore, it is crucial to carefully select appropriate RNN variants and deep learning frameworks for the implementation.

Pengendalian Biaya Kedelai Home Industry Tahu-Tempe Mbak Sri

This study aims to analyze the material cost control in Mbak Sri's home industry of tofu and tempeh production. Material costs constitute a significant portion of the total production costs, and effective control measures are crucial for ensuring profitability. The research employs a qualitative approach, utilizing observation and in-depth interviews to gather data. The findings reveal that Mbak Sri implements several cost control strategies, including careful material selection, efficient utilization, and waste minimization. However, challenges arise due to fluctuating raw material prices and limited bargaining power. The study recommends implementing a comprehensive material management system, establishing strategic supplier relationships, and exploring alternative sourcing options to enhance cost control measures. By addressing these recommendations, Mbak Sri's home industry can improve its overall operational efficiency and profitability.

Potential of water-soluble chitosan Schiff bases extracted from Metapenaeus dobsoni shells as effective corrosion inhibitors

Corrosion causes significant economic losses and structural failures in industries, highlighting the need for eco-friendly inhibitors. Chitosan (CS), a biodegradable and non-toxic biopolymer, shows potential, though its limited water solubility restricts its applications. To overcome this challenge, this study presents the synthesis of two water-soluble chitosan Schiff bases (CSBs) derived from the shells of Metapenaeus dobsoni (M. dobsoni). The extracted CS exhibits a remarkable degree of deacetylation exceeding 95 %, which was subsequently modified through reactions with o-vanillin (2-hydroxy-3-methoxybenzaldehyde) (CSB I) and 2,3-dihydroxybenzaldehyde (CSB II). Structural characterization using spectroscopic techniques confirmed the successful formation of CSBs. Electrochemical measurements were employed to assess the corrosion resistance of mild steel in 0.5 M HCl with varying concentrations of CSB I and CSB II. The results revealed superior corrosion inhibition by CSB II (% IE = 94.48 %) compared to CSB I (% IE = 88.80 %). The methoxy group in CSB II contributed to its higher electron density and enhanced adsorption, leading to better surface coverage and corrosion resistance. Both inhibitors followed the Langmuir isotherm, suggesting a mix of physisorption and chemisorption. These CSBs are promising for corrosion control in industries like pipelines, storage tanks, construction materials, and acid pickling.

In-space cybernetical intelligence perspective on informatics, manufacturing and integrated control for the space exploration industry

The integration of cybernetic principles into space technology has led to a significant shift in spacecraft guidance systems and space station operations. This scholarly work provides a comprehensive analysis of the profound impact that cybernetics has had on space technology, particularly focusing on the development and implementation of closed-loop control systems. Building on foundational contributions to cybernetics, this paper offers a detailed analysis of the complex interplay between control mechanisms, behavioral dynamics, and information management in space informatics. The essential role of cybernetic control systems is highlighted through their critical function in enabling precise spacecraft maneuvers, as demonstrated by the guidance systems used in Apollo lunar missions during the crucial descent and landing phases. In modern space station programs, cybernetic intelligence operates much like a neural network, processing data in real-time and coordinating the various elements of the station's infrastructure. This cybernetic system excels in a wide range of tasks, from space docking maneuvers to waste management, thereby safeguarding astronaut welfare and ensuring mission success. Information serves as the cornerstone of problem analysis and processing, providing a holistic understanding of information flow within complex systems. The study concludes by highlighting the application of a cybernetically intelligent neural network in developing adaptive learning systems for nonlinear and intricate industrial processes. Ultimately, this paper underscores the transformative role of cybernetics in shaping the future of space exploration, emphasizing the seamless integration of control, information, and behavior in space technology to advance our understanding of celestial systems while enhancing the efficiency and safety of space missions.

Differences in Salmonella serovars response to Lactic Acid and Peracetic Acid treatment applied to pork.

Pathogen control in the meat industry relies on the effectiveness of post-harvest interventions in reducing microbial populations. This study investigated differences in the survival of Salmonella serovars when exposed to organic acids used as antimicrobials on raw pork meat. Seven serovars were included in this study (S. Newport, S. Kentucky, S. Typhimurium, S. Dublin, S. Heidelberg, S. Infantis, and S. Enteritidis).Multi-strain serovar cocktails were prepared and tested against lactic acid (LA) and peracetic acid PAA at two concentrations, LA 2 and 4% and PAA 200 and 400 ppm. Pork samples were assigned to each serovar, inoculated with 6.0 Log CFU/cm2Salmonella (one serovar at a time), and treated with the corresponding antimicrobials. A two-way analysis of variance was conducted to examine the effects of serovar and antimicrobial concentrations on Salmonella survival. A significant main effect of serovar was identified, indicating that Salmonella concentration and reduction rate were significantly affected by serovar. Similarly, a significant main effect of antimicrobials was observed, suggesting that the treatment types impacted Salmonella concentration and reduction rate. However, the interaction effect between serovar and antimicrobial was not significant. Post-hoc comparisons indicate that PAA 400 ppm is more effective at reducing Salmonella concentrations and that S. Dublin may be more susceptible than S. Newport to antimicrobial sprays. Additionally, under PAA exposure, only S. Dublin, S. Kentucky, and S. Heidelberg showed statistically significant differences (P<0.05) compared with the control, indicating that these three serovars are more susceptible to PAA treatments than the rest. The behavior of different Salmonella serovars under stress conditions can give us an insight into how these pathogens survive processing.