Industrial Systems Research Articles

Gamma Process Based Degradation Model with Fractional Gaussian Noise

In modern industrial and engineering systems, stochastic degradation models are widely used for reliability analysis and maintenance decision-making. However, due to imperfect sensors and environmental influences, it is difficult to directly observe the latent degradation states. Traditional degradation models typically assume that measurement errors have simple statistical properties, but this assumption often does not hold in practical applications. To address this issue, this paper constructs a degradation model based on the Gamma process (GP) and assumes that measurement noise can be characterized by the fractional Gaussian noise (FGN). Furthermore, this paper proposes a method combining Gibbs sampling with the stochastic expectation-maximization (SEM) algorithm to achieve efficient estimation of the model parameters and accurate inference of the latent degradation states. Simulation results indicate that the proposed model exhibits better generalizability compared to the GP model with Gaussian noise.

Comparing Feature and Trajectory-Based Remaining Useful Life Modeling of Electrical Resistance Heating Wires

Industrial heating significantly contributes to global greenhouse gas emissions, accounting for a substantial portion of annual emissions. The transition to fossil-free operations in the heating industry is closely linked to advancements in industrial electrical heating systems, especially those using resistance heating wires. In this context, Prognostics and Health Management is crucial for enhancing system reliability and sustainability through predictive maintenance strategies. The integration of machine learning technologies into Prognostics and Health Management has significantly improved the precision and applicability of Remaining Useful Life modeling. This improvement enables more accurate predictions of component lifespans, optimizes maintenance schedules, and enhances operational efficiency in industrial heating applications. These developments are essential for reducing greenhouse gas emissions in the sector. This paper serves as a guide for conducting Remaining Useful Life modeling for industrial batch processes. It evaluates and compares two methodologies: deep learning-based approaches using full time-series data, such as recurrent neural networks and their variants, and feature-engineering-based methods, including random forest regression and support vector machines. Our results show that the feature-oriented approach performs better overall in terms of predictive accuracy and computational efficiency. The study includes a detailed sensitivity analysis and hyperparameter estimation for each method, providing valuable insights into developing robust and transparent Prognostics and Health Management systems. These systems are crucial in supporting the heating industry’s move towards more sustainable and emission-free operations. The findings reveal that feature-oriented methods are both performant and robust, particularly excelling in handling outliers. The random forest regression model, in particular, demonstrated the highest performance on the test dataset according to the chosen evaluation metrics. Conversely, trajectory-oriented methods exhibited less bias across varying levels of degradation, a helpful characteristic for Prognostics and Health Management systems. While feature-oriented methods tend to systematically underestimate Remaining Useful Life at high true values and overestimate it at low actual values, this issue is less pronounced in trajectory-oriented models. Overall, these insights highlight the strengths and limitations of each approach, guiding the development of more effective and reliable predictive maintenance strategies.

The Impact of 5G and Data Science on Digital Marketing: An Analytical Review and Research Agenda

Objective: This study aims to evaluate the impact of 5G technology across various sectors, focusing on its applications in smart cities, digital marketing, and industrial use cases, with the goal of understanding how 5G enhances connectivity, efficiency, and user experience. Theoretical Framework: The research is based on theories of digital transformation and emerging technologies, highlighting the role of artificial intelligence and advanced connectivity in the evolution of urban, commercial, and industrial systems. Method: A systematic literature review (SLR) was conducted, analyzing studies published from 1996 to 2023. The research utilized social network analysis, content analysis, and business case analysis to gain a comprehensive understanding of the effects of 5G. Results and Discussion: The findings reveal that 5G technology significantly improves connectivity, operational efficiency, and user experience, facilitating real-time communication and advanced resource management. The discussion contextualizes these findings within the theoretical framework, highlighting the benefits and challenges of 5G. Implications of the Research: The study emphasizes implications for digital infrastructure and services, suggesting the need for strategic investments and policies to fully leverage the potential of 5G. Originality/Value: This study provides an integrated and novel view of 5G, distinguishing itself from previous research by offering a holistic perspective on advanced applications.

RTPL: A Real-Time Communication Protocol for LoRa Network

The industrial Internet of Things (IIoT) is prominently emerging in applications of large-scale and wide-area applications, such as oilfield management, smart grid management, real-time equipment monitoring, and integration of traffic management systems for smart cities. Relying on short-range wireless technologies (e.g., WirelessHART and ISA100.11a), traditional wireless solutions for industrial automation find it challenging to support the expansive scale of today’s IIoT. To address this limitation, we propose to adopt LoRaWAN, a prominent low-power wide-area network technology, for industrial automation. LoRaWAN for industrial automation poses some unique challenges. The fundamental building blocks of any industrial automation system are feedback control loops that largely rely on real-time communication. LoRaWAN traditionally adopts a simple protocol based on ALOHA with no collision avoidance or Listen Before Talk with Clear Channel Assessment and Random Backoff mechanisms to minimize energy consumption, which are less suitable for real-time communication. Existing real-time protocols for short-range technologies cannot be applied to a LoRaWAN network due to its unique characteristics such as asymmetry between downlink and the uplink spectrum, predefined modes (or classes) of operation, and concurrent reception through orthogonal spreading factors. In this paper, we address these challenges and propose RTPL - a Real-Time communication Protocol for LoRaWAN networks. RTPL is a low-overhead and conflict-free communication protocol allowing autonomous real-time communication of low-energy devices and exploits LoRa’s capability of parallel communication. We implement our approach on LoRa devices and evaluate through both physical experiments and extensive simulations. All results show that RTPL achieves on average 75% improvement in real-time performance without sacrificing throughput or energy compared to traditional LoRaWAN.

Large Eddy Simulation of large-scale hydrogen deflagrations using the Thickened Flame Model with stretch sensitivity adaptation and thermo-diffusive instability modeling

Due to the high costs associated to experimental testing of hydrogen (H2) explosions, numerical simulations play an essential role in the design of safe industrial systems. In this work, we investigate the use of the Thickened Flame Model (TFM) to perform simulations of H2 deflagrations. Specific issues arise when considering hydrogen: (i) due to differential diffusion, the flame is very sensitive to stretch; (ii) instabilities of the flame front are generated and lead to an increased flame propagation speed. Both of these phenomena are affected by flame thickening. First, the flame sensitivity to stretch is artificially increased by the thickening. The recently developed Ma-TFM model proposes a correction of diffusive fluxes to reach the correct Markstein length and is selected in this work. Secondly, thermo-diffusive and hydrodynamic flame instabilities are also altered by thickening. By computing the dispersion relationship for a hydrogen/air mixture, we show that the instability scales are proportional to the thickening factor F. A simple subgrid scale (SGS) model, based on the computations of laminar unstable 2-D and 3-D flames, is then considered to take into account the effect of instabilities. The Ma-TFM and SGS instability models are applied together for the first time on a semi-industrial vented lean hydrogen deflagration, leading to promising results. In particular, it is found that considering 3-D unstable flames is necessary to accurately predict the experimental pressure peaks.

Penerapan Sistem Informasi Terpadu dalam Industri Tape Desa Banjarsari Menggunakan Celestial Cartography sebagai Peta Navigasi Kunjungan

The implementation of an integrated information system in the tape industry in Banjarsari Village using Celestial Cartography as a navigation map aims to enhance efficiency and visitor experience. This program integrates information technology with digital navigation to provide accurate and interactive guidance for visitors. The methods used include the development of a digital map application, technical training for industry managers, and evaluation of system effectiveness through user feedback. The results show increased visitor satisfaction and more efficient visit management. The implementation of this integrated information system is expected to support the development of a more modern and attractive tape industry in Banjarsari Village.

Guest Editorial Enabling Technologies and Systems for Industry 5.0: From Foundation Models to Foundation Intelligence

Guest Editorial Enabling Technologies and Systems for Industry 5.0: From Foundation Models to Foundation Intelligence

Industrial Metaverse-Based Intelligent PID Optimal Tuning System for Complex Industrial Processes.

In this article, the method of dynamic performance monitoring and adaptive self-tuning of parameters for actual PID control systems of industrial processes in virtual reality scenes is proposed. This method combines the digital twin model of the PID control process based on system identification and adaptive deep learning and the PID tuning intelligent algorithm based on reinforcement learning with virtual reality and immersive interaction of industrial metaverse. An industrial metaverse-based intelligent PID tuning system is proposed by combining the above method with the end-edge-cloud collaboration technology of Industrial Internet. The challenging problem that the actual operating PID control system in complex industrial processes cannot be optimized online is solved. Using the energy-intensive equipment, the fused magnesium furnace, as an industrial object, we conducted comparative simulation experiments between the proposed control method and several advanced control methods, as well as industrial experiments for the proposed intelligent system. Simulation experiments demonstrate the effectiveness of the proposed control method. The industrial experimental results indicate that the performance monitoring and adaptive self-tuning of parameters for actual PID control systems of industrial processes in virtual reality scenes can be realized, which achieves excellent control effects.

The Circular Industrial Transformation System (CITS) model - Assessing the life cycle impacts of climate and circularity strategies

The circular economy (CE) was introduced as a solution to mitigate increasing resource demand and to reduce environmental impacts. However, it remains a challenge to holistically assess long-term environmental impacts of CE strategies in complex, dynamic systems. To tackle this issue, we present the Circular Industrial Transformation System (CITS) model. CITS integrates dynamic stock modelling, material flow analysis and prospective life cycle assessment while being flexibly applicable to different products, materials and industry sectors across temporal and spatial scales. With that, the CITS model can assess the effect of circular strategies on long-term material flows and their respective environmental impacts, while including the effects of socio-economic developments, transformative climate policies, and a changing energy system. As a case study, the environmental impact reduction of both CE and climate change mitigation strategies was assessed for the German passenger car fleet until 2050. The results indicate that the occurring electrification of the passenger fleet is an effective strategy for reducing the global warming impacts of the automotive sector in the long-term, albeit aligned with the renewable energy transformation. CE strategies are most effective in reducing CO2-eq. emissions in the short term. Particularly, CE strategies affecting the vehicle stock promise substantial reductions in CO2-eq. emissions and primary material demand, while improved collection, sorting, and recycling have a limited impact. The results show that the CITS model can guide policies in effectively reducing environmental impacts in complex, dynamic systems by identifying system bottlenecks, trade-offs or synergies in industrial transitions.

Machine learning for reducing noise in RF control signals at industrial accelerators

Industrial particle accelerators typically operate in dirtier environments than research accelerators, leading to increased noise in RF and electronic systems. Furthermore, given that industrial accelerators are mass produced, less attention is given to optimizing the performance of individual systems. As a result, industrial accelerators tend to underperform their own hardware capabilities. Improving signal processing for these machines will improve cost and time margins for deployment, helping to meet the growing demand for accelerators for medical sterilization, food irradiation, cancer treatment, and imaging. Our work focuses on using machine learning techniques to reduce noise in RF signals used for pulse-to-pulse feedback in industrial accelerators. Here we review our algorithms and observed results for simulated RF systems, and discuss next steps with the ultimate goal of deployment on industrial systems.

Investigating the Catastrophic Roof Failure of a Fatty Acid Storage Tank: Causes, Mechanisms, and Safety Implications

This paper investigates the catastrophic failure of the roof weld on a carbon steel storage tank at a Fatty Acids plant, which was attributed to pressure overload. The incident resulted in the tank's roof, weighing over 5 tons and spanning 8 meters in diameter, being propelled 25 meters from the tank. Despite installing several safety devices, including pressure regulating valves, pressure indicator controls, and pressure vacuum relief valves, inadequate maintenance led to their malfunction. This study examines the mechanisms and causes behind the failure, highlighting the crucial role of regular maintenance in ensuring the functionality of safety instruments. While neither toxic nor flammable, the plant's compressed air system poses a significant hazard due to the energy stored at slight overpressure, necessitating vigilant management by the safety department. The findings underscore the importance of robust maintenance strategies and procedural adherence to prevent similar incidents, enhancing industrial storage systems' overall safety and reliability.

New metrics for measuring 2D uniformity in stirring system based on reconstruction of the particle trajectory

In industrial mixing systems, the evaluation method is a crucial indicator for measuring mixing uniformity. This study proposes an assessment method that combines a method for positioning using dual cameras and a point pattern density fluctuation (PD) method based on disordered hyperuniformity. This study employs a method for positioning using dual cameras to investigate the motion trajectories of tracer balls and achieve a 3D reconstruction process. The reconstructed 3D systems are cross-sectioned through on the axis of the stirring paddle to form a 2D planar diagram of the tracer ball trajectories. The PD method is used to evaluate the mixing effectiveness of the 2D trajectory points, establishing evaluation metrics for the 2D mixing system. To ensure the accuracy of the PD method, a feasibility analysis was conducted using the conductometry. A relationship model between λ and mixing time was established, with a Pearson correlation coefficient (r) of -0.9867, indicating a strong negative linear correlation. The relationship between the planes and whole 3D space was analyzed in terms of the whole and the discrete perspective, as well as the existing methods were compared with other approaches. This study not only deepens the theoretical understanding of evaluating mixing uniformity but also provides a crucial foundation and theoretical basis for process optimization of stirring systems and design of stirred reactors. It offers valuable references and insights for engineering design and technological improvements in related fields.

Adversarial Weight Prediction Networks for Defense of Industrial FDC Systems

Adversarial Weight Prediction Networks for Defense of Industrial FDC Systems

A Novel Transformer Winding Design Strategy for Efficiency Improvement and Harmonic Sup-Pression in Industrial Power System

A Novel Transformer Winding Design Strategy for Efficiency Improvement and Harmonic Sup-Pression in Industrial Power System

Global sustainable closed-loop supply chain considering Incoterms rules and advertisement impacts

Industrial information integration plays a crucial role in modern supply chains by ensuring the smooth flow of data across all stages, including recovery, recycling, and disposal, which is essential for the successful implementation of a closed-loop supply chain (CLSC) model. Building on this, our paper addresses a global CLSC problem by incorporating International Commercial Terms (Incoterms) and international transportation modes, bridging global supply chain operations with sustainability criteria. This innovative approach advances the development of a globally sustainable CLSC by focusing on the integration of economic, environmental, and social factors, i.e., the triple bottom line of sustainability. Specifically, we address environmental concerns through the introduction of carbon taxation and enhance social sustainability by exploring the impact of advertising on customer satisfaction. To further refine this model, we classify customers based on their sustainability engagement and apply a fuzzy programming approach to account for uncertainty in customer demand influenced by advertising. To solve this complex global CLSC model, we conduct a thorough analysis of constraints and develop a robust Lagrangian relaxation reformulation. While the initial solution may result in infeasibility, we propose a heuristic algorithm that ensures feasible solutions. Our efficient Lagrangian-based heuristic, incorporating an adaptive strategy, is capable of solving large-scale networks with an approximate 10% optimality gap. Ultimately, this research provides both a comprehensive framework for practitioners to improve the environmental performance and global operations of their supply chains, as well as significant theoretical contributions to the field of industrial information systems.

Implicit modular coupled heat transfer analysis for functionally graded materials using the SVC-FMC method

Functionally graded materials have found extensive applications in the aerospace industry and solar energy systems due to their excellent performance in enhancing heat transfer/insulation. Developing corresponding theoretical frameworks for the coupled radiation and conduction heat transfer (CRCHT) is crucial for unlocking their full potential and expanding their applicability in various high-performance and critical applications. Considering the limitations of existing frameworks in addressing CRCHT problems in practical applications, such as the explicit iteration procedure and the linearization assumption, a fully implicit framework as the source value caching function Monte Carlo (SVC-FMC) method is proposed based on the null collision reverse Monte Carlo and the Green's Function Markov Superposition Monte Carlo, which are suitable for solving the radiative transfer equation and the energy equation within non-uniform media. The accuracy of the proposed method is validated with different heat transfer systems. It was observed that the maximum root mean square error during the verification process did not exceed 0.08, thereby demonstrating the efficacy of SVC-FMC in analyzing CRCHT processes involving gradient materials. Results reveal that the material properties that transcend the functional distribution form will augment the strengthening effect of heat transfer/insulation. The scattering characteristics significantly influence the strengthening effect caused by refractive index distribution.

Integrated physical safety–cyber security risk assessment based on layers of protection analysis

The extensive application of information technology in process industries has increased production efficiency but has also introduced new risks. Therefore, it is necessary to systematically analyse the risks within factories to ensure the stable operation of their production systems. This study proposes an integrated risk assessment method based on layers of protection analysis (LOPA), which combines physical safety and cyber security analyses to provide comprehensive risk assessments for the process industry. The method first identifies the hazardous scenarios and protection layers relevant to a process facility. It then identifies potential cyberattack types and existing countermeasures. Subsequently, the functional impacts of attacks on protection layers and potential coupling relationships are discussed. Using common vulnerability scoring system (CVSS) and semi-quantitative methods, the probability of attack is determined to optimize the probability of failure on demand (PFD) of the protection layers. Finally, a case study of a steam separator in a catalytic cracking unit is used to quantitatively explore the potential attacks and risks of coupled protection layers. The application of Bayesian network (BN) is used for further validation of the method. This study offers a novel quantitative tool for risk assessment in the process industry, which can enhance the security and reliability of industrial production and control systems.

Enfoque multitareas implementado en una minicomputadora para el control y monitoreo de un motor a pasos industrial

This paper presents the implementation of the multitasking approach on a Raspberry Pi single board minicomputer, with experimental results, of the control and monitoring of an industrial type Nema 34 stepper motor through a user interface that allows the configuration of the control operation and data visualization to interact with the physical devices. One of the tasks consists of generating a pulse width modulation that is sent to the DQ860HA driver to rotate the stepper motor considering 400 pulses per revolution, while the second task simultaneously counts the number of pulses produced by an encoder with a resolution of 100 pulses per revolution, which is coupled to the motor shaft. The use of low-cost computer equipment and open-source software makes the presented development can be considered as a frugal technology application for the control and monitoring of an industrial system, with experimental results that validate the correct operation of two tasks that must be executed simultaneously.

Separation of adhesive joints of pouch cells in the context of battery module disassembly

The increase in demand for electric vehicles due to the transformation to electromobility will lead to a large number of batteries reaching the end of life and needing to be disposed of. Direct recycling of batteries requires disassembly down to cell level. However, adhesive bonds present a major obstacle to mechanical disassembly. In this work, adhesive bonds between pouch cells are characterised and possible separation processes are identified. Based on this, an industrial system concept for cutting the adhesive bonds, particularly with a rope cut, is developed and tested. A stable and safe process parameter space was identified. The separation process can enable further circular economy strategies such as remanufacturing or reusing the battery cells.

CONTRATOS INTELIGENTES: OPORTUNIDAD PARA AVANZAR HACIA NUEVOS MODELOS DE NEGOCIO EN LA INDUSTRIA

Blockchain is an emerging technology with great potential to provide new opportunities for industry thanks to its distributed, persistent and immutable nature. It enables the interaction of industrial systems without the need for a central authority, operating cooperatively through nodes in the network. This technology ensures the security and veracity of interactions (transactions), maximising resilience against cyber-attacks.