Industrial Systems Research Articles

Influence of robot-guide strategies on the geometry of directed energy deposition components

Directed energy deposition (DED) for additive manufacturing applications is commonly realized by the usage of industrial robots. The DED processing end effectors are usually mounted on industrial robot systems and can, therefore, be moved and oriented in up to many degrees of freedom. However, the design of the powder nozzle and the programming of the robot can limit the movement options. For this reason, translational movements, as with conventional 3D printers, are still common today. The end effector is usually guided horizontal over the component surface although welding in position (PA) is preferred in general. It may be necessary to tilt the end effector in order to gain advantages during processing due to the constrained position. This is particularly advantageous for overhang structures and is often realized with the help of a turn-tilt positioning table in combination with an industrial robot. However, this approach is, in some cases, not possible due to geometrical constrains. To extend applications in this direction, advanced methods for slicing the components and programming the robot movements are necessary. The main aspect of this work is the development, testing, and evaluation of a multidimensional DED manufacturing approach. This is tested on a thin-walled component with defined overhang areas and compared with conventional approaches. Different strategies are assessed in terms of the geometrical match of the target geometry. Influences of strategies on the results are evaluated. It can be shown that multidimensional path planning approaches lead to a better match of the target geometry.

Submerged and completely open solid–liquid triboelectric nanogenerator for water wave energy harvesting

AbstractTriboelectric nanogenerator (TENG) is an emerging wave energy harvesting technology with excellent potential. However, due to issues with sealing, anchoring, and difficult deployment over large areas, TENG still cannot achieve large‐scale wave energy capture. Here, a submerged and completely open solid–liquid TENG (SOSL‐TENG) is developed for ocean wave energy harvesting. The SOSL‐TENG is adapted to various water environments. Due to its simple structure, it is easy to deploy into various marine engineering facilities in service. Importantly, this not only solves the problem of difficult construction of TENG networks at present, but also effectively utilizes high‐quality wave energy resources. The working mechanism and output performance of the SOSL‐TENG are systematically investigated. With optimal triggering conditions, the transferred charge (Qtr) and short‐circuit current (Isc) of SOSL‐TENG are 2.58 μC and 85.9 μA, respectively. The wave tank experiment is taken for fully demonstrating the superiority of the SOSL‐TENG network in large‐scale collection and conversion of wave energy. Due to the excellent output performance, TENG can harvest wave energy to provide power for various commercial electronic devices such as LED beads, hygrothermograph, and warning lights. Importantly, the SOSL‐TENG networks realizes self‐powered for electrochemical systems, which provides a direction for energy cleanliness in industrial systems. This work provides a prospective strategy for large‐scale deployment of TENG applications, especially for harvesting wave energy in spray splash zones or at the surface of the water.image

Integration of Neuro-Fuzzy Systems in Medical Diagnostics and Data Security - A Review

Adaptive Neuro-Fuzzy Systems (ANFS) have become increasingly prevalent in a variety of fields due to their ability to process complex and uncertain data with high accuracy. This research article reviews three major contributions of ANFS: their application in deep neuro-fuzzy systems (DNFS) for healthcare and industrial systems, neuro-fuzzy logic controllers for paralysis estimation, and ANFIS-based solutions for secure cloud storage in medical IoT (MIoT). The findings emphasize the importance of ANFS in improving decision-making, diagnosis, and data security. This paper concludes with a discussion on challenges, future research directions, and the need for optimization in real-time applications.

Effect of droplet charge density on stabilization of oil‐in‐dispersion emulsions co‐stabilized by binary mixed surfactants and nanoparticles

AbstractIonic surfactant and similarly charged nanoparticles can co‐stabilize oil‐in‐dispersion (OID) emulsions at extremely low concentrations (0.001 cmc/0.001 wt%), in which particles do not adsorb at the oil/water interface but distribute in the aqueous phase forming a dispersion. In this paper, the effect of droplet charge density on stabilization of the n‐decane‐in‐water OID emulsion was examined by using a cetyl trimethyl ammonium bromide (CTAB)/C12B (dodecyl dimethyl carboxyl betaine) binary mixture at a low fixed total concentration (0.01 mM) with varying molar fractions of CTAB. A model based on the Derjaguin‐Landau‐Verwey‐Overbeek (DLVO) theory is proposed to calculate interaction energies between droplets and between droplets and particles. It is found that the droplet charge density can be well compensated by particle concentration along the stabilization boundary, and the OID emulsion still follows the DLVO stabilization. Particles tend to surround droplets at large distances but may form a monolayer between approaching droplets at shorter distances, which significantly reduces the van der Waals attraction between droplets. In addition, the induced auxiliary droplet–particle repulsion is proportional to the number of particles per unit area of droplet surfaces, which together with the droplet–droplet repulsion ensures a large total repulsion preventing droplets from flocculation and coalescence. This work explains quantitatively the stabilization of OID emulsions, which have potential applications in emulsion products such as foods, cosmetics, pesticides, and various industrial emulsion systems. Moreover, the development of the OID emulsions represents an important advancement in green chemistry as it substantially reduces the required amounts of emulsifiers and their environmental impact after use.

Artificial intelligence driven cyberattack detection system using integration of deep belief network with convolution neural network on industrial IoT

In Industry 4.0, information and communication technology (ICT) was employed in numerous significant infrastructures, like financial networks, smart factories, and power plants, to automate and certify industrial systems. In power control systems, ICT technologies such as IIoT have improved automated monitoring, but legacy methods, originally autonomous, now connect with external networks. This progress has presented safety vulnerabilities from legacy ICT systems. Hence, various cybersecurity approaches are developed and examined to deal with cyberattacks and vulnerabilities. Utilizing new cybersecurity models in power control systems poses risks due to their uncertified safety. Ensuring their stability and efficiency is significant for maintaining reliable power delivery and incorporating these technologies into power control systems. Therefore, this study designs a Next–Generation Cybersecurity Attack Detection using an ensemble deep learning model (NGCAD-EDLM) technique in the IIoT environment. The main cause of the NGCAD-EDLM technique is the automatic recognition of cyber-attacks. In the NGCAD-EDLM approach, the primary data normalization phase utilizing min-max normalization is performed. Next, the honey-badger algorithm (HBA) approach selects the feature subsets. Furthermore, an ensemble deep learning (DL) of two methods, namely convolutional neural networks (CNNs) and deep belief networks (DBNs) methods, are employed for classification. In addition, the DL techniques' hyperparameter selection is accomplished using the lotus effect optimization algorithm (LEOA) method. A complete set of simulation validation is performed to establish the experimental analysis of the NGCAD-EDLM method. The performance validation of the NGCAD-EDLM method exhibited a superior accuracy value of 99.21 % over other existing techniques.

Improving Deceptive Patch Solutions Using Novel Deep Learning-Based Time Analysis Model for Industrial Control Systems

Improving Deceptive Patch Solutions Using Novel Deep Learning-Based Time Analysis Model for Industrial Control Systems

Industrial Fault Detection Systems

This project presents an industrial fault detection system that monitors important electrical and environmental conditions to improve equipment reliability and safety. Using an ESP32 microcontroller, the system checks for issues like fuse and Miniature Circuit Breaker (MCB) status, as well as under and over-voltage situations with a ZMPT101B sensor. It also tracks temperature and humidity levels using a DHT11 sensor. The system displays real-time information on a 16x2 LCD and can be accessed via a web page, allowing operators to monitor conditions from anywhere. By quickly detecting electrical faults and environmental changes, this system helps reduce downtime and maintenance costs.

Research on the recycling decision of WEEE in rural China under dual supervision

Against the backdrop of promoting green, low-carbon, and high-quality development, this paper aims to promote efficient recycling of waste electrical and electronic equipment (WEEE) in rural China. In this paper, considering the integrated development of urban and rural areas and the standardization of industrial recycling system, under the joint action of the extension of producer responsibility and “dual regulation” of supply and marketing cooperatives, the evolutionary game model and system dynamics model of three-level recycling network of farmers, supply and marketing cooperatives and retailers are established. The mechanism of the participants to promote the recycling of WEEE in rural areas is discussed and the strategic choices and interactive relationships of various entities in the evolutionary process were used to analyze through the evolutionary game method. Meanwhile, using the theory of system dynamics, the main influencing factors of different evolution stages and the dynamic change process of the system are analyzed. The results show that: (1) supply and marketing cooperatives, retailers, and farmers can initially tend to participate in and supervise the recycling of WEEE; however, (2) they can finally achieve strong supervision, actively undertake and participate in the recycling and stabilization stable strategy of rural WEEE depends on their benefits and cost of expenditure expenditures are reasonable. (3) The strategic choice of supply and marketing cooperatives has the most significant impact on the strategic choice of retailers and the strategic choice of retailers has the most significant impact on the strategic choice of farmers.

A Martingale Posterior-Based Fault Detection and Estimation Method for Electrical Systems of Industry

The improvement of information sciences promotes the utilization of data for process monitoring. As the core of modern automation, time-stamped signals are used to estimate the system state and construct the data-driven model. Many recent studies claimed that the effectiveness of data-driven methods relies greatly on data quality. Considering the complexity of the operating environment, process data will inevitably be affected. This poses big challenges to estimating faults from data and delivers feasible strategies for electrical systems of industry. This paper addresses the missing data problem commonly in traction systems by designing a martingale posterior-based data generation method for the state-space model. Then, a data-driven approach is proposed for fault detection and estimation via the subspace identification technique. It is a general scheme using the Bayesian framework, in which the Dirichlet process plays a crucial role. The data-driven method is applied to a pilot-scale traction motor platform. Experimental results show that the method has good estimation performance.

Advancing Anti-Biofilm Strategies: Innovations to Combat Biofilm-Related Challenges and Enhance Efficacy.

Biofilms are complex communities of microorganisms that can cause significant challenges in various settings, including industrial processes, environmental systems, and human health. The protective nature of biofilms makes them resistant to traditional anti-biofilm strategies, such as chemical agents, mechanical interventions, and surface modifications. To address the limitations of conventional anti-biofilm methods, researchers have explored emerging strategies that encompass the use of natural compounds, nanotechnology-based methods, quorum-sensing inhibition, enzymatic degradation, and antimicrobial photodynamic/sonodynamic therapy. There is an increasing focus on combining multiple anti-biofilm strategies to combat resistance and enhance effectiveness. Researchers are continuously investigating the mechanisms of biofilm formation and developing innovative approaches to overcome the limitations of conventional anti-biofilm methods. These efforts aim to improve the management of biofilms and prevent infections while preserving the environment. This study provides a comprehensive overview of the latest advancements in anti-biofilm strategies. Given the dynamic nature of this field, exploring new approaches is essential to stimulate further research and development initiatives. The effective management of biofilms is crucial for maintaining the health of industrial processes, environmental systems, and human populations.

Enhanced fault detection in energy systems using individual contextual forgetting factors in recursive principal component analysis

Improving maintenance strategies is an important component in reducing wastes of energy in industrial systems and especially energy systems, as they represent a large proportion of our total energy use. The use of fault detection methods can help transition to proactive maintenance methods and automate the detection of faults using data from the systems themselves. However, the presence of normal operational changes must be accounted for to avoid false alarms, as static fault detection methods cannot handle them. A recursive fault detection method, specifically recursive principal component analysis (RPCA), is applied to real building data in this paper to aid in addressing this challenge. Additionally, improvements to the aspect of updating forgetting factors, which are integral to RPCA's functionality, is also proposed which allows for enhanced adaptability. The results show that the improvement increases adaptability when setpoints are changed and provides similar performance otherwise. Lastly, the application of this was shown to significantly reduce false alarms in the building application, while still detecting the known faults.

Mode 2 critical systems practice for complex safety decisions: Reflections from New Zealand's dairy industry

AbstractPoor decision‐making contributes to disasters in complex industrial systems. Improving group decision‐making practices for safety‐critical interventions is crucial yet hindered by a fragmented safety science literature. Our study applies Jackson's critical systems practice (CSP) to a complex safety problem at a New Zealand dairy manufacturing facility. Reflective practice, employed in the study and writing this article, underlines CSP's usefulness in navigating complex emergent safety challenges. Key insights include clarifying cognitive complexity amongst participants early in an intervention, focusing more sharply on crucial safety outcomes by using critical facilitation and enhancing CSP's ‘Check’ stage using reflective practice. We conclude by calling on scholars to prioritise the application of CSP in real‐world contexts to deepen our understanding of systemic praxis and generate actionable improvements.

Research on Multi-Sensor Fusion Fault Diagnosis Method Based on Spatiotemporal Attention Mechanism

As industrial systems become increasingly complex, real-time monitoring and intelligent management of industrial equipment have become imperative. However, the limitations in coverage and accuracy of single sensors make it challenging to comprehensively characterize the operational state of equipment, leading to reduced system reliability and increased pressures on data transmission and storage. To address these challenges, this study presents a novel fault diagnosis method based on multi-sensor fusion using a spatio-temporal attention mechanism. Initially, one-dimensional convolutional neural networks (1D-CNN) are employed to extract features from raw signals, effectively capturing local characteristics and ensuring the integrity and validity of fault signals. Subsequently, the spatiotemporal attention mechanism adjusts the feature weights based on the temporal and spatial correlations of different sensors, as well as their respective importance, thereby capturing the spatio-temporal dependencies across multiple sensors and enhancing the efficacy of information fusion. Finally, the proposed method is validated through experiments on a nickel flash smelting furnace system. The results demonstrate that the method achieves a fault diagnosis accuracy exceeding 97.78%, significantly enhancing fault detection and decision-making performance.

ICS-LTU2022: A dataset for ICS vulnerabilities

Industrial control systems (ICS) are a collection of control systems and associated instrumentation for controlling and monitoring industrial processes. Critical infrastructure relies on supervisory control and data acquisition (SCADA), a subset of ICS specifically designed for monitoring and controlling industrial processes over large geographic areas. Cyberattacks like the Colonial Pipeline ransomware case have demonstrated how an adversary may compromise critical infrastructure. The Colonial Pipeline ransomware attack led to a week’s pipeline shutdown, causing a gas shortage in the United States. As existing vulnerability assessment tools cannot be used in the context of ICS systems, vulnerability datasets specified for ICSs are needed to evaluate the security weaknesses. Our secondary metadata, ICS-LTU2022, consists of multiple features that can be used for vulnerability assessment and risk evaluation in industrial control systems. A description of the dataset, its characteristics, and data analysis are also presented in this paper. Vulnerability analysis was conducted based on the top 10 vulnerabilities in terms of severity, frequency by year, impact, components of the ICS, and common weaknesses. The ICS-LTU2022 vulnerabilities dataset is updated biannually. Our proposed dataset provides security researchers with the most recent ICS critical vulnerabilities.

Quality, Safety, and Security Systems in the Greek Port Industry: Over Twenty Years of Research, Empirical Evidence, and Future Perspectives

Background: Quality, Safety and Security are embedded in all aspects of port operations and are crucial for port industry stakeholders. Over the past decades, numerous assurance systems, codes, and regulations for quality, safety, and security have been developed and implemented in ports world-wide. This paper examines key insights for the implementation those systems in ports, reviewing over 20 years of empirical research on the impact and outcomes of such systems in Greek ports and internationally. Methods: It compares and discusses evidence from two empirical surveys spanning the first two decades of the 21rst century. The first survey was conducted in 2011 (with evidence from 2000 to 2011) including 12 major Greek ports (SAs) and the second one was conducted up to 2022–2023, including 23 over 25 Greek ports (the same SAs and other ones from the Greek TEN-T network). Results: The higher-level scope of this paper is to investigate critical perspectives on and trends in quality, safety, and security systems in the port industry. Conclusions: This investigation aims to strengthen the assumption that quality, safety, and security play a pivotal role in shaping the image, performance, and growth of ports.

Enhancing healthcare data integrity: fraud detection using unsupervised learning techniques

Data in healthcare forms the backbone of any treatment and decision-making for patients. However, the data from a healthcare institution can sometimes be prone to abnormalities, thereby putting treatment and patient safety in jeopardy. This paper points to the dire need for reliable anomaly detection systems in the healthcare industry. It employs various unsupervised learning methods, including Isolation Forest, Local Outlier Factor (LOF), K-Nearest Neighbours (KNN), and autoencoder models for detecting abnormalities in healthcare data with better accuracy. Anomaly detection capabilities also allow health providers to reduce risks and provide some assurance of the integrity of the data, as these capabilities are more likely to indicate unusual patient profiles or incorrect test results. Isolation Forest, LOF, and KNN are the preliminary methods for performing anomaly detection in this work, with Isolation Forest yielding the best results. Then, autoencoder models that learn subtle variations and complex patterns in data are employed. This paper seeks to enhance anomaly detection in terms of accuracy and reliability to ensure better healthcare data quality and patient safety.

International Approaches to the Development, Validation, and Change Management of Analytical Procedures (Review)

INTRODUCTION. In 2023, the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) published a new Guideline on Analytical Procedure Development (ICH Q14) and a revised version of the Guideline on Validation of Analytical Procedures (ICH Q2(R2)). Consequently, there is a need for a considerable revision of the approach to the development and validation of analytical procedures that is currently used in the Eurasian Economic Union (EAEU). A revision is also needed for the processes for evaluating and introducing variations to the analytical procedures described in medicinal product registration dossiers.AIM. This review aimed to analyse the significant changes made to international approaches to the development of analytical procedures, as well as to study the advantages and disadvantages of these approaches for pharmaceutical manufacturers and regulatory agencies in the EAEU.DISCUSSION. This review covers the key provisions and practical aspects of the enhanced approaches to the development of analytical procedures introduced by the ICH Q14 guideline. In particular, the review addresses the concepts of the analytical procedure life cycle (APLC) and the modified analytical qualityby-design (AQbD) approach; the development of the analytical target profile (ATP); analytical quality risk management; planning of the design of experiments (DoE) and the analytical procedure control strategy; and the validation, subsequent verification, transfer, and change management of analytical procedures. Additionally, the review describes the ICH Q2(R2) updates that accompany this new regulatory paradigm.CONCLUSIONS. The above guidelines fill the existing gap in recommendations for the development of analytical procedures. The use of the APLC and AQbD concepts provides both pharmaceutical companies and regulatory authorities with flexible approaches that are applicable to analytical procedures both during the development phase and once they have been implemented. Effective implementation of these international approaches in the Russian pharmaceutical industry and regulatory system requires a broad discussion between pharmaceutical industry professionals and regulatory agency experts, possibly, as part of a pilot project. After that, there will be a necessity to provide training for specialists involved in the development of analytical procedures and to amend the EAEU Rules for Marketing Authorisation and Expert Assessment of Medicinal Products for Human Use.

Different paths to improving together: a taxonomy of buyer-supplier collaborations for sustainability in food supply networks

Purpose Extant research shows collaboration among supply chain (SC) partners can address the significant environmental impacts of industrial food systems, but can be risky and resource-intensive. Past studies have predominantly treated buyer–supplier sustainability collaborations as a single aggregate concept missing the theoretical richness. This study aims to provide a more nuanced understanding of these collaborations for jointly improving sustainable food supply networks. Design/methodology/approach A multiple case study was conducted covering 8 SCs in the Dutch food processing industry, using data from 27 interviewees and extensive secondary material. Findings By applying the dynamic relational view, this study identifies three types of buyer–supplier collaboration, reflecting three paths of sustainable value creation: (1) the bilateral path, featuring equal participation and extensive collaboration, yielding substantial environmental and economic benefits; (2) the buyer-driven path, where the buyer leads the collaboration to address sustainability issues that are raised by stakeholders, relying on supplier expertise to improve SC traceability and sustainability reputation, albeit at higher costs; (3) the supplier-driven path, where the supplier leads the collaboration for incremental environmental and economic improvements. Practical implications The taxonomy results provide practical guidelines to assist managers in selecting the most suitable collaboration type for their specific sustainability goals and more effectively address sustainability challenges. Originality/value The three identified types of collaboration form a novel taxonomy for improving sustainability in food supply networks, representing different paths for SC partners to achieve progressively more substantial sustainability improvements. This taxonomy challenges the perspective that adopting sustainability invariably leads to increased costs by providing evidence of simultaneous economic and environmental improvements.

Thermo-hydraulic performance of concentric tube heat exchangers with turbulent flow: Predictive correlations and iterative methods for pumping power and heat transfer

This research addresses the problem of predicting the thermo-hydraulic performance of concentric tube heat exchangers (CTHE) under turbulent flow conditions, a critical aspect in energy-efficient industrial systems such as HVAC, power generation, and chemical processing. Existing studies often lack accurate predictive methods for balancing heat transfer performance with pumping power requirements. To tackle this issue, novel correlations and an iterative Newton–Raphson method were developed for predicting pumping power and heat transfer rates. Three-dimensional CFD simulations of a water-to-water counter-flow CTHE were conducted, with Reynolds numbers ranging from 4000 to 8000 for both the hot and cold fluids. The simulations employed the Reynolds-Averaged Navier–Stokes (RANS) equations with the k−ω SST turbulence model. The results demonstrated that increasing the Reynolds number enhances both heat transfer rates and pumping power, with the cold fluid requiring consistently higher pumping power. New correlations were developed to predict pumping power, capturing the impact of both entry and fully developed flow regions. These correlations showed an average error of less than 2.33% when compared with the CFD data. The iterative Newton–Raphson method for predicting heat transfer rates demonstrated high accuracy, with an average error of 0.66% for heat transfer rate, 0.03% for hot fluid outlet temperature, and 0.01% for cold fluid outlet temperature. Additionally, we identified optimal operating conditions for efficient cooling and heating based on the heat capacity ratio (Cr). The novelty of this work lies in the development of new, highly accurate predictive correlations and iterative methods for optimizing CTHE performance, going beyond existing literature by providing comprehensive insights into the relationship between pumping power, heat transfer efficiency, and flow conditions.

CPDet: Circle-Permutation-Aware Object Detection for Heat Exchanger Cleaning

Shell–tube heat exchangers are commonly used equipment in large-scale industrial systems of wastewater heat exchange to reclaim the thermal energy generated during industrial processes. However, the internal surfaces of the heat exchanger tubes often accumulate fouling, which subsequently reduces their heat transfer efficiency. Therefore, regular cleaning is essential. We aim to detect circle holes on the end surface of the heat exchange tubes to further achieve automated positioning and cleaning tubes. Notably, these holes exhibit a regular distribution. To this end, we propose a circle-permutation-aware object detector for heat exchanger cleaning to sufficiently exploit prior information of the original inputs. Specifically, the interval prior to the extraction module extracts interval information among circle holes based on prior statistics, yielding prior interval context. The following interval prior fusion module slices original images into circle domain and background domain maps according to the prior interval context. For the circle domain map, prior-guided sparse attention using prior a circle–hole diameter as the step divides the circle domain map into patches and performs patch-wise self-attention. The background domain map is multiplied by a hyperparameter weak coefficient matrix. In this way, our method fully leverages prior information to selectively weigh the original inputs to achieve more effective hole detection. In addition, to adapt the hole shape, we adopt the circle representation instead of the rectangle one. Extensive experiments demonstrate that our method achieves state-of-the-art performance and significantly boosts the YOLOv8 baseline by 5.24% mAP50 and 5.25% mAP50:95.