Industrial Systems Research Articles

Development and Programing of LCD Digital Clock with “RTC - Real Time Clock Module”

In the digital era, precise timekeeping is critical across a large variety of applications, from personal devices and hobby electronics to industrial systems. This article presents the development and programming of a digital clock using a Real-Time Clock (RTC) module, a core component that ensures accurate time maintenance even if the device in which it is used is not connected to the power source.My research focuses on the integration of the RTC module using a microcontroller, emphasizing on the reliability of timekeeping provided by this hardware. My article can serve as a guide for hobbyists and enthusiasts looking to create their own digital clocks. It’s also useful for students engaged in electronics and programming. This project gives creatives the opportunity to further develop it or even completely redesign the entire PCB. By combining practical and theoretical knowledge and skills, I aim to demonstrate the importance of RTC modules in modern timekeeping devices and inspire innovation in digital clock designs.

Exploring High-Performance Work Systems and Sustainable Development in the Hospitality Industry

This study examines the impacts of high-performance work systems (HPWS) on employee career decision-making self-efficacy (CDMSE) and service performance with regard to the theory of reasoned action in the hospitality industry. Recognizing the critical influence of human interaction and service quality on customer satisfaction, this study demonstrates that HPWS can lead to a motivated and skilled workforce, enhancing CDMSE and service excellence. Employing a quantitative research design, this study analyzes data collected from hospitality employees to assess the relationship between HPWS, CDMSE, and service performance. The findings reveal that HPWS significantly enhance employee service performance by boosting CDMSE, which in turn leads to proactive service behaviors. Furthermore, this study highlights the moderating role played by service climate, showing that a supportive environment amplifies the positive effects of HPWS on employee service outcomes. These insights contribute to an improved theoretical understanding of HR practices in the hospitality sector and offer practical implications for organizations aiming to improve service quality while supporting sustainable hospitality development.

Winning the battle with cyber risk identification tools in industrial control systems: A review

AbstractThe modern Industrial Control System (ICS) environment now combines information technology (IT), operational technology, and physical processes. This digital transformation enhances operational efficiency, service quality, and physical system capabilities enabling systems to measure and control the physical world. However, it also exposes ICS to new and evolving cybersecurity threats that were once confined to the IT domain. As a result, identifying cyber risks in ICS has become more critical, leading to the development of new methods and tools to tackle these emerging threats. This study reviews some of the latest tools for cyber‐risk identification in ICS. It empirically analyses each tool based on specific attributes: focus, application domain, core risk management concepts, and how they address current cybersecurity concerns in ICS.

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.

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.

Machine Learning Supporting Virtual Reality and Brain–Computer Interface to Assess Work–Life Balance Conditions for Employees

The widespread adoption of the Industry 5.0 paradigm puts people and their applications at the center of attention and, with the increasing automation and robotization of work, the need for workers to acquire new, more advanced skills increases. The development of artificial intelligence (AI) means that expectations for workers are further raised. This leads to the need for multiple career changes from life and throughout life. Belonging to a previous generation of workers makes this retraining even more difficult. The authors propose the use of machine learning (ML), virtual reality (VR) and brain–computer interface (BCI) to assess the conditions of work–life balance for employees. They use machine learning for prediction, identifying users based on their subjective experience of work–life balance. This tool supports intelligent systems in optimizing comfort and quality of work. The potential effects could lead to the development of commercial industrial systems that could prevent work–life imbalance in smart factories for Industry 5.0, bringing direct economic benefits and, as a preventive medicine system, indirectly improving access to healthcare for those most in need, while improving quality of life. The novelty is the use of a hybrid solution combining traditional tests with automated tests using VR and BCI. This is a significant contribution to the health-promoting technologies of Industry 5.0.

Evaluating the research and development (R&D) efficiency of the Chinese construction industry

PurposeThe construction industry has been investigating “where Henry Ford is in the industry system.” Given that listed construction enterprises are the backbone of the promotion of the high-quality development of the industry, their research and innovation are of considerable importance. This study aims to comprehensively assess the research and development (R&D) status quo and trends within various types of construction enterprises in order to identify effective strategies to enhance R&D efficiency in the construction industry.Design/methodology/approachBased on the data won from annual reports and the CSMAR database for the period 2016–2020, this study examines 104 listed construction enterprises in China. By applying both the data envelopment analysis (DEA) method and the Malmquist productivity index, this research compares and analyzes the static and dynamic differences in R&D efficiency across different types of construction enterprises.FindingsResults suggest that the magnitude of change in the Malmquist decomposition index of 104 listed construction enterprises gradually narrowed, but the comprehensive technological level remained relatively low. Although state-owned enterprises had an advantage in scale efficiency, meaning they could maximize output with given inputs, their technological progress efficiency, also known as the degree of technological innovation, was significantly lower than that of private enterprises. As one finding, state-owned enterprises in comparison with private enterprises experience significant R&D inefficiency. It represents the main cause of their low degree of technological innovation and efficiency.Originality/valueThis study assesses the R&D efficiency of listed construction enterprises in China from the perspective of different market segments, state-owned and private enterprises and suggests approaches to improve strategies for various corporate types. Thus, the study’s new findings contribute to addressing the challenge of low R&D levels in the construction industry in the fields of engineering, construction and architectural management.

Building integrated assessment model for IoT technology deployment in the Industry 4.0

Internet of Things (IoT) platforms have become the building blocks of any automated system but they are more important in case of industrial systems where sensitive data are captured and handled by the information system. Therefore, it is imperative to deploy the right IoT platform to perform the computational and operational tasks in a better way. During the last few years, an array of IoT technologies/platforms with different capabilities and features were introduced in the markets. This abrupt rise created selection and decision-making issues particularly for the network engineers, designers, and industrial managers due to a lack of technical understanding and skill in this area. Therefore, we present an integrated assessment model focusing on evaluating and ranking IoT platforms in the industrial environment. It encompasses multiple methods such as the proposed model leverages a well-known data collection technique such as Delphi for data collection related to the criteria features. It adopts the Analytic Hierarchy Process (AHP) for giving weights to the criteria features. The technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method has been applied for the evaluation of the top twenty (20) Industrial IoT(IIoT) platform alternatives according to the proposed criteria. It selects the most rational choice of IoT platform that can be employed in the Industry 4.0 setting. The proposed integrated assessment model produces the most accurate and consistent outcomes. Hence, it is believed that it can be used as a guideline by different stakeholders like researchers, developers, network engineers, and policymakers for the assessment and deployment of IoT platforms in the industrial environment. It is believed that it is the first kind of multi-methods integrated assessment mode for the assessment, decision-making, and prioritization of IoT technologies in the industry 4.0 domain.

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.

Optimizing Sustainable Manufacturing: An Integrated Approach of Green Information’s Systems and Knowledge-Based Decisions in Industry 4.0: A Comparative Analysis

Adopting green information technology (IT) systems has become crucial for improving the environmental sustainability of manufacturing firms. To drive the manufacturing process more appropriately, management and administrators recognize the importance of developing green IT systems aligned with knowledge management (KM) practices. To accomplish the study objective, experts were engaged in a study conducted in Henan manufacturing firms to share their knowledge on the factors that influence management’s intention to adopt green IT systems concerning KM. The study collected 324 valid responses from industrial experts in Henan manufacturing industries through survey questionnaires. Using structural equation modeling (SEM), the hypotheses were evaluated, and the significance of each factor in the model was determined. The SEM results revealed that the KM process interpretation significantly influences green IT systems to improve manufacturing process development. Furthermore, the study found that combining KM processes and green practices has a more substantial impact on improving the manufacturing process. This suggests that integrating knowledge management practices with green IT systems can lead to better environmental sustainability and overall performance in manufacturing firms. The findings of this study offer valuable insights for decision-makers and policymakers in formulating policies and programs to implement a green information system with green implementation processes effectively in the manufacturing sector. By adopting these strategies, manufacturing firms can contribute to environmental sustainability while simultaneously improving their overall performance.

Model-Based Assessment of Energy Efficiency in Industrial Pump Systems: A Case Study Approach

Outdated, oversized variable speed pump drives (VSDPs) in industry lead to sub-optimal energy efficiency and considerable energy losses. This paper proposes methods to develop 2D efficiency maps for motors, converters, and pumps using polynomial surface fitting, which enables efficiency evaluation in a wide operating range. The method was applied to an oversized VSDP in an industrial chilled water supply system, comparing the original system with five alternative VSDP combinations with high-efficiency motors and pumps. The five VSDP variants demonstrated average energy savings of around 30%, with the synchronous reluctance motor (SRM) configurations outperforming the induction motor (IM) configurations by up to 7 percentage points, particularly at low loads. The high-efficiency SRM-based 252-IE5 variant delivered the best overall energy performance, highlighting the benefits of optimised system sizing and motor selection for energy savings. The proposed method can be used in both industrial and residential applications and offers great advantages in process systems that require variable flow and pressure of water or other fluids during operation, such as HVAC, water supply and wastewater treatment, district heating, etc. The development of a VSDP drive with efficient energy optimisation is an interdisciplinary problem of mechanical and electrical engineering, and without the interaction of engineers from both fields the result will not be optimal. We try to present our method so that it can be a reliable tool for mechanical, electrical, and other engineers or researchers to assist them in finding possible energy savings, performing energy audits, and selecting the most suitable components when modernising existing or developing new systems.

Acoustic analysis of a three-dimensional cylindrical shell model under electromagnetic vibration

This paper presents the acoustic analysis of a three-dimensional cylindrical shell model under electromagnetic vibration, a critical factor affecting the performance of electric motors in various applications such as automotive, aerospace, and industrial systems. The study provides a multidisciplinary approach that integrates electromagnetics, structural vibration, and acoustics, solved using the fast multipole boundary element method (FMBEM). The results summarize the validation of the analytical models and numerical simulations, offering insights into effective vibration reduction methods. The conclusions indicate that the 3-D numerical analysis using FMBEM aligns well with the analytical solution for the sound pressure in the exterior acoustic domain of the cylindrical shell model. The paper contributes valuable insights for the design of low-noise motors and the control of electromagnetic vibration and noise in electric motors.

System-level Prognostics and Health Management for Complex Industrial Systems

Prognostics and health management (PHM) has become essential to guarantee aware and safe system operation and to inform economic decision-making. However, due to the nature of detection, diagnostics, and prognostic methods, applications have mainly been limited to the component level. In practice, most industrial systems consist of multiple interacting components whose partial degradation could lead to the system's failure (or subsystems).This research addresses the limitations of traditional component-level PHM techniques by proposing a novel system-level framework. By implementing a hierarchical structure of components and subsystems, we will select an optimal method for each subsystem to aggregate its component health assessments. The overall system health can then be estimated by further combining the obtained estimates. The research considers simplified and holistic modeling techniques, margin-based methods, and hybrid graphical models. This approach aims to provide reliable system health predictions and online components' sensitivity measures to enhance maintenance decision-making. We consider an application in the context of the nuclear industry, characterized by strict safety and economic requirements. Using a SIMULINK model to approximate a Pressurized Water Reactor (PWR) with real industrial inputs, we plan to add component degradation modules and use simulated sensor data and reliability information to test the proposed framework. Initial results on artificial case studies show the feasibility of integrating component-level health predictions.

Performa Termal Kondensor Udara pada Wind Tunnel: Evaluasi Perpindahan Panas

Heat transfer in air condenser systems is a critical aspect in various industrial applications, especially refrigeration and air conditioning systems. The thermal efficiency of condensers is greatly influenced by airflow characteristics, but airflow velocity optimization remains a challenge due to the trade-off between improved heat transfer and energy consumption. Although previous research has examined various aspects of condenser design, there is still a need to explore the optimal limit of airflow velocity that can maximize thermal efficiency without excessively increasing system workload. This study aims to evaluate the thermal performance of an air condenser in a wind tunnel, focusing on the effects of airflow velocity variations on heat transfer rate and thermal efficiency. The experimental method uses a wind tunnel with dimensions of 280x28x28 cm, equipped with a precise temperature and flow velocity measurement system. The flow velocity was varied from 0.8 to 1.8 m/s. The results showed a significant increase in the heat transfer coefficient (h) and heat transfer rate (Q) as the flow velocity increased. Analysis of Reynolds and Nusselt numbers revealed the transition of the flow from laminar to turbulent, which contributed to the increase in heat transfer efficiency. Comparison of experimental results with simulations showed good agreement in the middle velocity range, but there were deviations at extreme velocities. In conclusion, increasing the airflow velocity proved effective in improving the thermal performance of the condenser, but further optimization is required to balance the thermal efficiency with the energy consumption of the system.

Uncertainty-Aware Prediction of Remaining Useful Life in Complex Systems

Accurate prediction of the remaining useful life (RUL) of industrial systems is critical to ensuring smooth operation and safety. Various prognostic methods have been developed, but significant challenges remain for field applications. While many methods may achieve high accuracy, they often fall short in quantifying the uncertainty of their predictions. Without uncertainty quantification, it is difficult to assess the confidence level of the prognostic results. Therefore, it is essential to transparently present the uncertainty levels in the predicted results. This Ph.D. project aims to develop novel uncertainty-aware methods for RUL prediction of complex systems. The project will address the following situations where it is more and more uncertain: (a) propose a general framework for data-driven RUL methods to quantify uncertainty and generate adaptive confidence intervals under a single fault mode and a single operating condition; (b) consider both epistemic and aleatoric uncertainties in scenarios with multiple fault modes and multiple operating conditions and then calibrate uncertainty to enhance their accuracy; (c) explore how to predict RUL and quantify uncertainty when there are no run-to-failure data and RUL labels in practice; (d) handle uncertainty propagation from the component level to the system level. Through this research, the project will provide more reliable and comprehensive solutions for RUL prediction in complex systems.

Advanced Harmonic Analysis in Industrial Power Systems: Its Impact on Enhancing Power Quality and System Performance

This paper presents an advanced harmonic analysis of industrial power systems, focusing on its impact on enhancing power quality and system performance. The study investigates the harmonic distortions caused by nonlinear loads, particularly those introduced by Variable Frequency Drives (VFDs), in a manufacturing plant. Measurements of key electrical parameters, such as voltage, current, and Total Harmonic Distortion (THD), were recorded and analysed. The results reveal significant harmonic distortion levels, exceeding industry standards, which can lead to reduced equipment efficiency and potential damage. By adhering to IEEE 519 guidelines and implementing harmonic mitigation strategies, such as filters and power factor correction, the paper demonstrates how power quality can be improved. The findings provide a foundation for future simulation and optimization efforts aimed at reducing harmonic distortion and enhancing system performance. Keywords: Total Harmonic Distortion (THD), Harmonic Analysis, Power Quality, Industrial Power Systems, Current Distortion.

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.

Advanced Gaming using Eye Gesture Recognition

Eye gesture recognition represents a advanced technology in the evolution of human-computer interaction (HCI) and for advanced gaming purpose, ‘particularly in the Scenario of today’s industry, equipping delicate hardware such as’ infrared cameras’, ‘depth sensors’, and ‘electrooculography’ (EOG), with the advance software algorithms including Convolutional Neural Networks (CNN), gaze estimation algorithm, and real-Time tracking frameworks like Filters Kalman.’ By including machine learning, deep learning, and computing algorithms, this technology enabled precise interpretation of gestures, establishing interaction between ‘augmented and virtual reality of today’s tech world’, and set the stage for enhanced user experience in intelligent and human Centric industrial system. ‘This type of development marked by primary challenges such as: the accurate eye detection and the creation of a suitable sign language for eye gesture using movements of the pupil. This research focused on the utilization of CNN Algorithm technique to address these challenges, accounting for differentiation in pose, Orientation, Location, and Scale’. The system detects the eye gesture, pre-processing the image extracted from dataset from platform named Kaggle. The subsequent image analysis is performed using ‘python programming’ and OpenCV, utilizing the theories of eye detection and segmentation to enhance the accuracy of system. ‘The proposed methodology also integrates the histogram-based approach to differentiate among various machine learning algorithms and provide the optimal results for eye gesture analysis’

Permutation Entropy: An Ordinal Pattern-Based Resilience Indicator for Industrial Equipment.

In a highly dynamic and complex environment where risks and uncertainties are inevitable, the ability of a system to quickly recover from disturbances and maintain optimal performance is crucial for ensuring operational continuity and efficiency. In this context, resilience has become an increasingly important topic in the field of engineering and the management of productive systems. However, there is no single quantitative indicator of resilience that allows for the measurement of this characteristic in a productive system. This study proposes the use of permutation entropy of ordinal patterns in time series as an indicator of resilience in industrial equipment and systems. Based on the definition of resilience, the developed method enables precise and efficient assessment of a system's ability to withstand and recover from disturbances. The methodology includes the identification of ordinal patterns and their analysis through the calculation of a permutation entropy indicator to characterize the dynamics of industrial systems. Case studies are presented and the results are compared with other resilience models existing in the literature, aiming to demonstrate the effectiveness of the proposed approach. The results are promising and highlight a highly applicable and simple indicator for resilience in industrial systems.

A multiobjective optimization model for allocating water quantity and quality in the Yangtze and Yellow River basins

Rapid urbanization and industrial growth have led to water quantity and quality problems. Most current water allocation models focus solely on physical water and neglect the optimization of the water quantity and quality considering the water footprint. Therefore, this study aimed to establish an optimal water resource allocation model for industrial sectors based on the water footprint to identify the most effective strategies for determining the water quantity and quality distribution in river basins. First, we constructed an improved water footprint accounting system using input‒output tables to quantify the total water footprint, grey water footprint, and virtual water trade. Second, we constructed a multiobjective optimal water resource allocation model by considering the water footprint as a decision variable and combining regional economic development and productivity differences. Finally, we compared the water allocation results obtained for the Yellow River Basin, a typical “water-quantity water-scarce region” in China, with those of the Yangtze River Basin, a “water-quality water-scarce region.” The results indicate that the total water footprints of the Yellow and Yangtze River Basins were 211.37 and 317.83 billion m³, respectively, prior to optimization. After optimization, the footprints were 199.54 and 306.03 billion m³, respectively. Water resources have been reallocated through virtual water trade strategies to effectively alleviate both water quantity and quality problems. Virtual water trade strategies have emerged as policy tools capable of mitigating mismatches between industrial systems and regional water resource allocation and providing a solution to physical–virtual water system challenges within river basins.