Industrial Systems Research Articles

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.

A multi-criteria trust-enhanced collaborative filtering algorithm for personalized tourism recommendations

The exponential growth of online information has LED to significant challenges in navigating data overload, particularly in the tourism industry. Travelers are overwhelmed with choices regarding destinations, accommodations, dining, and attractions, making it difficult to select options that best meet their needs. Recommender systems have emerged as a promising solution to this problem, aiding users in decision-making by providing personalized suggestions based on their preferences. Traditional collaborative filtering (CF) methods, however, face limitations, such as data sparsity and reliance on single rating scores, which do not fully capture the complexity of user preferences. This study proposes a hybrid multi-criteria trust-enhanced CF (HMCTeCF) algorithm to improve the accuracy and robustness of tourism recommendations. HMCTeCF improves the quality of recommendations by integrating multi-criteria user preferences with trust relationships among users and between items. Experimental results using real-world datasets, including Restaurants-TripAdvisor and Hotels-TripAdvisor, demonstrate that HMCTeCF outperforms benchmark CF-based recommendation methods. It achieves higher prediction accuracy and coverage rate, effectively addressing the data sparsity problem. This innovative algorithm facilitates a more personalized and enriching travel experience, particularly in scenarios with limited user data. The findings highlight the importance of considering multiple criteria and trust relationships in developing robust recommendation systems for the tourism industry.

A preliminary Coasian proposal for tree auditing via planning control of tree farms

The emergence of green consumerism has contributed to the rise of farms which hold out as tree planters for absorbing CO2. A preliminary survey of the literature on tree farms and town planning practice of Western Australia found that there is scepticism regarding the credibility of these tree farms and an absence of an information flow system that rewards the credible private tree farmers. From a Coasian neoinstitutional economic (NIE) perspective, there is therefore the potential for devising rules that can enlarge an existing market or industry. This short commentary maps the current information flow system in the tree planting industry and proposes an improved system based on voluntary disclosure of tree planting data by tree farms in the planning permission system.

Wave-transparent and eco-friendly flame-retardant phosphorus-based phthalonitrile/quartz composites by a synchronized self-curing strategy with cyano and alkynyl groups

The robust human–machine interaction systems of the aerospace industry necessitate the development of thermal resistant wave-transparent composites, with a pivotal focus on organic resin matrix. However, enhancing the heat resistance of such materials while preserving other critical attributes has been a formidable challenge. In this study, a novel designed strategy of a dual-curing functional phosphorus-based phthalonitrile monomer (P-ALK-PN) has been proposed. The optimized curing temperatures for intra- or intermolecular Diels-Alder reactions of alkynyl groups and polyaddition reaction of cyano groups resulted in a homogeneous and highly crosslinked N-enriched phthalonitrile system. After curing at 450 °C, the resin, namely P-ALK-PN-450 °C, demonstrated exceptional thermal stability (T5% = 644 °C), thermo-oxidative stability (T5% = 554 °C), and char yield at 800 °C (90.7 % in N2 and 67.5 % in air). Their quartz fiber reinforced composites (P-ALK-PN/QFs) were subsequently fabricated by the hot-pressing process. Upon post-curing at 420 °C, P-ALK-PN-420 °C/QF displayed elevated glass transition temperature (550 °C), flexural strength (319 MPa), and relatively low dielectric properties with a dielectric constant (Dk) of approximately 4.2 and a dissipation factor (Df) less than 0.02 across a wide temperature ranging from 25 °C to 600 °C. Especially, it exhibited excellent flame retardancy (only a 6.7 % weight loss at ambient temperatures exceeding 1300 °C for 200 s) as well, stemming from release of eco-friendly inert gases (NH3) and formation of a graphitized protective layer during pyrolysis. These promising results highlight the potential applications of P-ALK-PN resins in aerospace and high-performance engineering fields.

A digital twin framework for anomaly detection in industrial robot system based on multiple physics-informed hybrid convolutional autoencoder

The robot number in industry is growing up rapidly. Building anomaly detection system for them can improve the security of these expensive devices. The article implements an anomaly detection framework based on digital twin, which are built by a hybrid convolutional autoencoder. The framework shares those neural network weight files as digital assets, users can use them to estimate the possible output from real input. It approximates the dynamic relationship between motion, current, temperature and vibration with hybrid convolution. Considering the limited generalization performance of direct data-driven methods in practical physical systems, this article introduces physical information methods to improve the constraint function of neural network. The influence of multiple physical fields on current is established by a unified neural network. Terminals detect anomaly with KL divergence between really current and estimated current. The article collects operational data from real robots and verifies it, and the experiment shows that the RMSE for current estimation is below 1.5 %, the F1-score in anomaly detection is over 98.23 %, false positive is below 1 %, false negative is below 1.7 %. The relevant technologies are gradually being promoted and applied in enterprises.

Remaining Useful Life Prediction of Aero-engine via Temporal Convolutional Network with Gated Convolution and Channel Selection Unit

As a hot spot in the prognostics and health management (PHM), predicting the remaining useful life (RUL) is significant to ensure the availability and reliability of industrial systems. Data-driven methods, such as the traditional temporal convolutional networks (TCNs), have been widely used for RUL prediction. However, traditional TCNs lack effective means to sufficiently extract both the spatial and temporal features of multi-sensory data. Furthermore, the prediction ability of traditional TCNs is constrained by their inflexible network structure. Here, an integrated model called the improved temporal convolutional network with gated convolution and channel selection unit (GCSU-ITCN) is proposed as a solution to the above problems. First, “the loss boundary to mapping ability” (LM) method is introduced to select sensors with superior mapping ability to the RUL. It contributes to the extraction of features from multi-sensory data. Then, the gated convolution and channel selection unit (GCSU) is designed to extract both local and global features of multi-sensory data, encompassing the spatial and temporal dimensions. Last, the improved temporal convolutional network (ITCN) is developed to capture long temporal correlations within the extracted features. The ITCN has a flexible structure that enables it to learn deep temporal features more effectively than traditional TCNs. A series of comparative experiments is conducted on the C-MAPSS dataset to evaluate the prediction capability of the GCSU-ITCN. Furthermore, ablation experiments are also conducted to assess the contribution of each component within the model.

The spatial-temporal evolution characteristics and influencing factors of the coupling between the industrial chain and innovation chain in China

Exploring the theoretical logic, development characteristics and improvement paths of the deep coupling of innovation chains and industrial chains (two-chain) is of great importance for building a modern industrial system and developing productive forces. However, empirical evidence on the spatial-temporal characteristics and influencing factors of two-chain coupling is still lacking. In this study, the degree of coupling coordination model is applied to assess the degree of two-chain coupling in China using provincial panel data from 2013 to 2022. In addition, spatial analysis is applied to investigate the spatial-temporal evolution characteristics of the variables in question. On this basis, the fuzzy set qualitative comparative analysis (fsQCA) method is applied to identify influential factors. The results indicate that: (1) Temporally, the coupling level of the two-chain shows a consistent upward trend and gradually develops a distribution pattern characterized by high values in the east and weak values in the west. (2) Spatially, there is a significant positive spatial correlation between the two-chain coupling. (3) In terms of drivers, the increase in two-chain coupling is attributed to multiple factors. Three models can be identified: human-driven, capital-driven and information-driven, which are influenced by the cooperation between government and market. The novelty of this article lies in its exploration of the theoretical logic and developmental characteristics of two-chain coupling, while empirically demonstrating its driving factors. This study provides a new perspective for governments and relevant departments to improve two-chain linkages and promote economic development.

Efficiency Performance of China Brand Automobiles: The Role of Innovation Systems

This research aims to examine and investigate the efficiency performance and its influencing factors of China brand automobiles’ innovation system using both theoretical and empirical approaches. Initially, the study applies the DEA model to assess the technical efficiency, pure technical efficiency, and scale efficiency of the innovation system of China brand automobiles. The results reveal a general upward trend in the innovation system’s efficiency of China brand automobiles. Subsequently, a panel regression model is employed to analyze the determinants impacting the efficiency of the innovation system. The findings of the research suggest that economic development, openness to international markets, and marketization enhance the technical efficiency of the innovation system of the domestic automobile industry. However, an increase in enterprise size tends to negatively affect this technical efficiency. While economic growth, openness, and marketization positively impact pure technical efficiency, the expansion of enterprise size exerts a detrimental effect. Conversely, enterprise size growth positively influences scale efficiency, whereas openness and marketization present some negative implications. Lastly, this study proposes policy recommendations from both governmental and enterprise viewpoints, aimed at promoting the sustainable advancement of the automobile industry.

An investigative study on the effective utilization of process hazard analysis (PHA) and safety instrumentation system (SIS) in chemical process industries

AbstractThis research paper focuses on effectiveness of process hazard analysis (PHA) and safety instrumented systems (SIS) interventions during process design and implementation which prevents accident and loss in chemical process industries. An initial preview was conducted to analyze the accidents and incidents in case of industrial and chemical process industries which enabled to define the research question in a scientific way. The research was conducted using a survey questionnaire developed with the help of focus group and was based on theoretical and conceptual framework developed for the study. The survey received responses from 36 participants who met the inclusion and exclusion criteria. The validity and reliability were established using confirmatory factor analysis, convergent validity, and Cronbach’s Alpha. The data analysis methods were used to answer the research questions to test the hypotheses. From the results it is observed that PHA and SIS were in an agreeable range. However, the performance factors grouped based on barrier controls and organizational behavior were have moderate positive relationship. This showed there exists a gap in the PHA and SIS contributing factors which may directly or indirectly cause industrial accidents in the chemical process industries.

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.

Innovative application of green surfactants as eco-friendly scale inhibitors in industrial water systems.

Scale deposition poses significant challenges in various industrial utilities, necessitating the development of eco-friendly scale inhibitors in line with environmental regulations. This study investigates the potential of two natural surfactants, Casein and Rhamnolipid, as innovative inhibitors for calcium carbonate (CaCO3) scale formation, offering an alternative to traditional water treatment chemicals. The anti-scaling characteristics of these two green surfactants were performed using conductivity and electrochemical impedance spectroscopy (EIS) techniques. Additionally, X-ray diffraction (XRD) and scanning electron microscopy (SEM) were utilized to analyze the morphology of CaCO3 crystals and understand the structural changes induced by surfactant interaction. The results revealed that Rhamnolipids significantly outperform Casein in suppressing scale formation, attributed to the adsorption of their multiple functional groups onto scale microcrystals. This adsorption modifies the crystal structure and inhibits further growth. Computational studies were employed to investigate the inhibition mechanism of these surfactants. The spatial and electrical configurations of optimal molecular structures are also analyzed using the Density Functional Theory (DFT) approach. Monte Carlo simulations on the CaCO3 (104) surface demonstrated that Rhamnolipids exhibit superior inhibition compared to Casein, as evidenced by their higher adsorption energy, indicating a more stable binding to the surface. Overall, this research highlights the potential of these natural surfactants as sustainable scale inhibitors, particularly in industries such as food and pharmaceuticals, thereby contributing to environmentally friendly water treatment solutions.

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.