Industrial Technology Research Articles

Research and application of intelligent diagnosis method for impeller fault based on centrifugal pump digital twin flow field cloud map

With the development of industrial technology, the demand for health diagnosis and maintenance of centrifugal pumps is becoming increasingly urgent. Combining digital twin and machine vision technology, this paper proposes an intelligent diagnosis method for centrifugal pump impeller machinery fault based on digital twin flow field cloud map. Firstly, the centrifugal pump digital twin model is used to simulate the evolution of random fracture fault of impeller blades, and the impeller flow field pressure and velocity cloud maps with different fault characteristics are generated; secondly, based on the learning and training of Yolov5 algorithm, two types of machine vision models of pressure and velocity cloud maps are obtained, and the preliminary diagnosis of impeller faults is realized by combining statistical analysis; then, considering the complementary advantages of the two types of detection models, the two are integrated based on the idea of stacking integration to improve the accuracy of impeller fault diagnosis. Experimental verification shows that for random fracture faults of impeller blades, the centrifugal spring intelligent fault diagnosis method proposed in this paper can achieve a diagnostic accuracy of more than 0.99, and the developed intelligent diagnosis system for centrifugal pump impeller machinery faults enables the method in this paper to be put into practice.

Influence of constant and alternating electric fields on the deformation and destruction of a liquid droplet

Examining the impact of electromagnetic field, which provides thrust in contemporary rocket engines, is turning into a significant issue. Its resolution is required to guarantee the safety of space flight by enhancing engine performance and lowering fuel consumption. Spacecraft propulsion is achieved by low-thrust rocket engines. The principle of operation for ion colloid engines is the electrostatic acceleration of charged droplets. A static electric field accelerates charged liquid droplets created by electrospraying them in a low-thrust colloid engine. A promising technology in many industries is the active control of droplet motion and deformation with electric field. An electromagnetic field impact on droplet deformation and destruction in a viscous liquid is examined. The effect of electromagnetic field on individual droplets and emulsions is examined in terms of their physical mechanisms. Constant and alternating electric field effects on liquid droplet are represented numerically. The effects of droplet electric capillary number and viscosity ratio on droplet unsteady deformations is explored. The parameters that correspond to the droplet destruction are found. The results obtained have potential applications in enhancing the efficiency of current industrial electric dehydrators and in advancing the development of new electromagnetic field-based demulsification technologies.

The impact of industry technology complexity on audit quality

With the continuous upgrading of Chinese industries, industry technology complexity has increased dramatically, to which audited units and audit subjects have adaptive responses, affecting audit quality. However, relevant studies have not been conducted. This paper takes A-share listed companies from 2007 to 2021 as a sample to investigate the impact of industrial technology complexity on audit quality and its mechanism. It is found that industry technology complexity can significantly enhance audit quality; in enterprises at the front-end of industrial technology, industry technology complexity has a more significant role in promoting audit quality. Adopting the demonstration zone for the transfer and transformation of scientific and technological achievements as an exogenous impact policy, the study finds that the impact of industry technology complexity on audit quality still holds. Further research finds that the adaptation of audited units and audit subjects to industry technology complexity has a positive incentive effect on audit quality. The findings extend the audit consequences of industrial technology complexity and provide empirical evidence for the role of industrial technology complexity in enhancing audit quality.

Fundamental Understanding of Marine Applications of Molten Salt Reactors: Progress, Case Studies, and Safety

Marine sources contribute approximately 2% of global energy-related CO₂ emissions, with the shipping industry accounting for 87% of this total, making it the fifth-largest emitter globally. Environmental regulations by the International Maritime Organization (IMO), such as the MARPOL (International Convention for the Prevention of Pollution from Ships) treaty, have driven the exploration of alternative green energy solutions, including nuclear-powered ships. These ships offer advantages like long operational periods without refueling and increased cargo space, with around 200 reactors already in use on naval vessels worldwide. Among advanced reactor concepts, the molten salt reactor (MSR) is particularly suited for marine applications due to its inherent safety features, compact design, high energy density, and potential to mitigate nuclear waste and proliferation concerns. However, MSR systems face significant challenges, including tritium production, corrosion issues, and complex behavior of volatile fission products. Understanding the impact of marine-induced motion on the thermal–hydraulic behavior of MSRs is crucial, as it can lead to transient design basis accident scenarios. Furthermore, the adoption of MSR technology in the shipping industry requires overcoming regulatory hurdles and achieving global consensus on safety and environmental standards. This review assesses the current progress, challenges, and technological readiness of MSRs for marine applications, highlighting future research directions. The overall technology readiness level (TRL) of MSRs is currently at 3. Achieving TRL 6 is essential for progress, with individual components needing TRLs of 4–8 for a demonstration reactor. Community Readiness Levels (CRLs) must also be addressed, focusing on public acceptance, safety, sustainability, and alignment with decarbonization goals.

An effective method for anomaly detection in industrial Internet of Things using XGBoost and LSTM

In recent years, with the application of Internet of Things (IoT) and cloud technology in smart industrialization, Industrial Internet of Things (IIoT) has become an emerging hot topic. The increasing amount of data and device numbers in IIoT poses significant challenges to its security issues, making anomaly detection particularly important. Existing methods for anomaly detection in the IIoT often fall short when dealing with data imbalance, and the huge amount of IIoT data makes feature selection challenging and computationally intensive. In this paper, we propose an optimal deep learning model for anomaly detection in IIoT. Firstly, by setting different thresholds of eXtreme Gradient Boosting (XGBoost) for feature selection, features with importance above the given threshold are retained, while those below are ignored. Different thresholds yield different numbers of features. This approach not only secures effective features but also reduces the feature dimensionality, thereby decreasing the consumption of computational resources. Secondly, an optimized loss function is designed to study its impact on model performance in terms of handling imbalanced data, highly similar categories, and model training. We select the optimal threshold and loss function, which are part of our optimal model, by comparing metrics such as accuracy, precision, recall, False Alarm Rate (FAR), Area Under the Receiver Operating Characteristic Curve (AUC-ROC), and Area Under the Precision–Recall Curve (AUC-PR) values. Finally, combining the optimal threshold and loss function, we propose a model named MIX_LSTM for anomaly detection in IIoT. Experiments are conducted using the UNSW-NB15 and NSL-KDD datasets. The proposed MIX_LSTM model can achieve 0.084 FAR, 0.984 AUC-ROC, and 0.988 AUC-PR values in the binary anomaly detection experiment on the UNSW-NB15 dataset. In the NSL-KDD dataset, it can achieve 0.028 FAR, 0.967 AUC-ROC, and 0.962 AUC-PR values. By comparing the evaluation indicators, the model shows good performance in detecting abnormal attacks in the Industrial Internet of Things compared with traditional deep learning models, machine learning models and existing technologies.

The Economy–Security Nexus: Risk, Strategic Autonomy and the Regulation of the Semiconductor Supply Chain

Abstract The EU’s policies in the field of technology broadly defined are increasingly marked by a concern over strategic autonomy, and Europe’s place in the world. Regulatory interventions are framed in terms of “digital sovereignty,” with the Commission seeking to ensure that external dependencies are reduced with the aim of increasing the EU’s resilience to geopolitical instability and external shocks. Using the case study of semiconductors, the chips that power modern electronic devices, this article explores how technology policy in the EU sits at the economy–security nexus, in which economic goals and security goals are interdependent and inseparable. Focusing on the life cycle of the semiconductor supply chain from the control over natural resources through to the cybersecurity requirements placed on the finished products, this article demonstrates the increasing security logic embedded within a burgeoning industrial technology policy.

Research on the Structural Design of a Pressurized Cabin for Civil High-Speed Rotorcraft and the Multi-Dimensional Comprehensive Evaluation Method

For civil high-speed rotorcraft designed to operate at specific cruising altitudes, this study proposes nine structural design schemes for pressurized cabins. These schemes integrate commonly used materials and processing technologies in the aviation industry with advanced PRSEUS (Pultruded Rod Stitched Efficient Unitized Structure) technology. An analysis of the structural composition reveals that frames constitute 8–19% of the total structural weight, while stringers and beams make up 15–50%, and skins account for 11–25%, with thicknesses ranging from 1.0 mm to 2.0 mm. The separating interface of the pressurized cabin contributes 4–29% of the total structural weight. The weight distribution of each component in the pressurized cabin structure varies significantly depending on the chosen materials and processing technologies. Utilizing the Analytic Hierarchy Process (AHP), along with Gray Relational Analysis (GRA) and Dempster–Shafer (D-S) evidence theory, this study compares the simulation results of the nine schemes across multiple dimensions. The findings indicate that the configuration combining 7075 aluminum alloy and T300 composite material has the greatest advantages in terms of the high structural reliability of the configuration, light weight, mature processing technology, and low production cost. This comprehensive evaluation method quantitatively analyzes the factors influencing the structural configuration design of the pressurized cabin for civil high-speed rotorcraft, offering a valuable reference for the design of similar structures in related fields.

Application of 360 Camera Technology and Excavator Operator Workload at a Nickel Ore Mine and Its Implication for Work Accident Rates Using the Nasa-TLX Method

Indonesia, as one of the largest nickel producers in the world, has around 90% of nickel reserves spread across various regions, such as Central Sulawesi, South Sulawesi, Southeast Sulawesi, and North Maluku. Even though demand for nickel continues to increase, a severe problem that needs attention is the rate of work accidents in mining fields. Most heavy equipment accidents that occur are caused by operator negligence while operating. This negligence can occur when operators are in a hurry, make mistakes while using heavy equipment, or do not have proper work time management. This research aims to analyze the impact of 360-degree camera technology on excavator operators in nickel ore mines on work accident rates and operator workload. The case study was carried out at PT Rifky and Raisha Anursyah using the NASA-TLX method to measure workload. In contrast, the number of accidents that occurred to each operator within one month was used to measure work accidents. The 360 camera technology provides a comprehensive view of the work environment, allowing operators to monitor the surrounding area better and reduce blind spots. The research results show that applying this technology can reduce the risk of work accidents and increase the work efficiency of excavator operators. With better monitoring, operators can identify potential hazards earlier and reduce the risk of accidents. Workload evaluation using NASA-TLX shows a decrease in operator workload after implementing 360 camera technology. This research provides strategic recommendations for workload management and implementing safety technology in the mining industry.

Emerging and Conventional Water Desalination Technologies Powered by Renewable Energy and Energy Storage Systems toward Zero Liquid Discharge

The depletion of fossil fuels has become a significant global issue, prompting scientists to explore and refine methods for harnessing alternative energy sources. This study provides a comprehensive review of advancements and emerging technologies in the desalination industry, focusing on technological improvements and economic considerations. The analysis highlights the potential synergies of integrating multiple renewable energy systems to enhance desalination efficiency and minimise environmental consequences. The main areas of focus include aligning developing technologies like membrane distillation, pervaporation and forward osmosis with renewable energy and implementing hybrid renewable energy systems to improve the scalability and economic viability of desalination enterprises. The study also analyses obstacles related to desalination driven by renewable energy, including energy storage, fluctuations in energy supply, and deployment costs. By resolving these obstacles and investigating novel methodologies, the study enhances the understanding of how renewable energy can be used to construct more efficient, sustainable, and economical desalination systems. Thermal desalination technologies require more energy than membrane-based systems due to the significant energy requirements associated with water vaporisation. The photovoltaic-powered reverse osmosis (RO) system had the most economically favourable production cost, while MED powered via a concentrated solar power (CSP) system had the highest production cost. The study aims to guide future research and development efforts, ultimately promoting the worldwide use of renewable energy-powered desalination systems.

Development of a methodology for assessing economically useful signs of hyssop

Hyssop (Hyssopus officinalis L.) refers to valuable spicy-aromatic, essential oil and medicinal crops and is widely used in traditional and folk medicine, perfumery, cosmetology, cooking and food industry. The main directions of selection of hyssop are increasing productivity, obtaining forms adapted to industrial cultivation technology, resistance to pests and diseases, valuable chemical and biochemical composition, different terms of onset of economic suitability, honey productivity, zoning (suitability for cultivation in specific soil and climatic conditions). The article presents the results of studies on the evaluation of the breeding material of hyssop by a complex of economically useful morphological, morphometric and phenological traits, including a new zoned author’s variety of the Belarusian State Agricultural Academy Zaveya. As a result of the research, the methodology for testing the distinctiveness, uniformity and stability of hyssop has been improved, which makes it possible to optimize the breeding process of this crop and conduct an extended assessment of the identification of varieties during state variety testing. The national methodology for testing for distinctiveness, uniformity and stability provides an opportunity for breeders to pay attention to the relevant characteristic and distinctive features of hyssop, which will contribute to the effective conduct of further breeding work to create forms and varieties with various economically valuable properties.

Pharmaceutical Wastewater and Sludge Valorization: A Review on Innovative Strategies for Energy Recovery and Waste Treatment

Currently, a large amount of pharmaceutical waste (PW) and its derivatives are being produced and, in some cases, inadequate management or treatment practices are applied. In this regard, this research explores the adoption of several alternatives to deal with these problems, including biocarbon within the framework of the circular economy. Photocatalytic nanomaterials have been also extensively discussed as a feasible way to remove pharmaceutical compounds in wastewater. Although there are existing reports in this area, this document provides a detailed study of the synthesis process, experimental conditions, the integration of photocatalysts, and their impact on enhancing photocatalytic efficiency. Additionally, the low cost and ease of fabrication of lab-scale microbial fuel cells (MFCs) are thoroughly examined. This innovative technology not only facilitates the degradation of hazardous compounds in wastewater but also harnesses their energy to generate electricity simultaneously. The aforementioned approaches are covered and discussed in detail by documenting interesting recently published research and case studies worldwide. Furthermore, this research is of significant importance because it addresses the valorization of PW by generating valuable by-products, such as H2 and O2, which can occur simultaneously during the photodegradation process, contributing to more sustainable industrial practices and clean energy technologies.

Comparative evaluation of the extracellular production of a polyethylene terephthalate degrading cutinase by Corynebacterium glutamicum and leaky Escherichia coli in batch and fed-batch processes

BackgroundWith a growing global population, the generation of plastic waste and the depletion of fossil resources are major concerns that need to be addressed by developing sustainable and efficient plastic recycling methods. Biocatalytic recycling is emerging as a promising ecological alternative to conventional processes, particularly in the recycling of polyethylene terephthalate (PET). However, cost-effective production of the involved biocatalyst is essential for the transition of enzymatic PET recycling to a widely used industrial technology. Extracellular enzyme production using established organisms such as Escherichia coli or Corynebacterium glutamicum offers a promising way to reduce downstream processing costs.ResultsIn this study, we compared extracellular recombinant protein production by classical secretion in C. glutamicum and by membrane leakage in E. coli. A superior extracellular release of the cutinase ICCGDAQI was observed with E. coli in batch and fed-batch processes on a litre-scale. This phenomenon in E. coli, in the absence of a signal peptide, might be associated with membrane-destabilizing catalytic properties of the expressed cutinase. Optimisations regarding induction, expression temperature and duration as well as carbon source significantly enhanced extracellular cutinase activity. In particular, in fed-batch cultivation of E. coli at 30 °C with lactose as carbon source and inducer, a remarkable extracellular activity (137 U mL−1) and cutinase titre (660 mg L−1) were achieved after 48 h. Literature values obtained with other secretory organisms, such as Bacillus subtilis or Komagataella phaffii were clearly outperformed. The extracellular ICCGDAQI produced showed high efficacy in the hydrolysis of PET textile fibres, either chromatographically purified or unpurified as culture supernatant. In less than 18 h, 10 g L−1 substrate was hydrolysed using supernatant containing 3 mg cutinase ICCGDAQI at 70 °C, pH 9 with terephthalic acid yields of up to 97.8%.ConclusionExtracellular production can reduce the cost of recombinant proteins by simplifying downstream processing. In the case of the PET-hydrolysing cutinase ICCGDAQI, it was even possible to avoid chromatographic purification and still achieve efficient PET hydrolysis. With such production approaches and their further optimisation, enzymatic recycling of PET can contribute to a more efficient and environmentally friendly solution to the industrial recycling of plastics in the future.

A socio-economic assessment of an emerging technology in the mining industry

Abstract Purpose This article provides methodological insights to evaluate the socio-economic risk of an emerging froth flotation technology for the mining sector with the goal of guiding the design and development process. This technology is used to separate valuable particles based on surface properties among minerals and, if properly developed, could be used to valorize fine particles that currently existing technology cannot separate and thus become waste material. Methods The Social Hotspot assessment utilized the Product Social Impact Life Cycle Assessment (PSILCA) database to analyze social hotspots in the relevant industrial sector. In addition, a survey captured the viewpoints of technology developers regarding additional potential social risk and opportunities. The final results were defined by combining these two analyses, conducted according to the 2020 UNEP guidelines for Social Life Cycle Assessment of Products and Organizations. For the economic assessment, the Material Flow Cost Accounting (MFCA) methodology (ISO14051) was applied, considering material costs, system costs, energy costs, and waste management costs for both the current situation and a future industrial-scale scenario. Results and discussion The study emphasizes the importance of tailoring methodological approaches for case studies involving low Technology Readiness Level (TRL) technologies based on available data. The state of technology development has led to different results for the economic and social analyses, primarily due to the difficulty in accurately predicting potential social impacts at this stage. The social analysis identified potential risks and 28 subcategories of impacts across different stakeholder categories. The economic assessment found that energy costs (49%) were the highest contributor to the MFCA cost of the future scenario, followed by system costs (29%). Conclusions and recommendations The study concludes that conducting a socio-economic analysis during the developmental stage of a technology is valuable for identifying critical hotspots that require monitoring, effectively guiding the research and development phase. This application represents a unique case in the mining sector and could be a first step in defining a methodological approach suitable for low TRL technologies. Analyzing both social and economic risks provides a more comprehensive perspective on sustainability, complementing environmental risk assessments.

Challenges of BIM Technology and Lean Theory in the Construction Industry in Vietnam

Challenges of BIM Technology and Lean Theory in the Construction Industry in Vietnam

Artificial intelligence methods used in various aquaculture applications: A systematic literature review

AbstractThis research article presents a systematic literature review on the current state‐of‐the‐art artificial intelligence (AI) methodologies used in aquaculture applications. As the demand for seafood continues to grow, the aquaculture industry faces numerous challenges, including disease management, feeding optimization, water quality monitoring, and extraction of aquaculture area. To address these challenges effectively and sustainably, AI techniques have been increasingly applied in aquaculture systems over recent years. This review aims to analyze various AI methodologies utilized within different aspects of aquacultural practices. By examining existing studies and identifying trends and gaps in research areas related to AI integration into aquaculture practices, this paper provides valuable insights for further advancements. The purpose was to synthesize current knowledge on application and its challenges in implementing AI technologies within the commercial aquaculture industry. Specifically, the review is to identify and analyze peer‐reviewed studies reporting on applications of AI technologies in aquaculture industry, to classify and summarize the key findings from the selected studies in aquaculture operations through AI, and to evaluate and discuss any challenges reported regarding the implementation and adoption of AI solutions in commercial aquaculture. The overall goal was to comprehensively assess these via a systematic literature review process. Challenges of AI technologies and methods were identified in the research literature for applying AI to optimize commercial aquaculture practices and production. An exhaustive search of a scholarly database from Scopus, was performed and papers published in English between 2020 and 2024 were considered for inclusion. After a rigorous screening process involving over 116 studies, 57 highly relevant works were identified and analyzed according to key themes involving demonstrated AI applications, employed methodologies and challenges that are expected when applying such methods. The findings revealed that AI‐driven tools such as computer vision, machine learning, and predictive modeling hold much potential for enhancing sustainability, efficiency, and productivity within aquaculture operations through applications like disease monitoring, environmental management, and production optimization. However, the review also uncovered substantial challenges that will continue limiting widespread adoption, including restricted access to representative data, prohibitive expenses, technical complexities, lack of social acceptance, and data privacy and security concerns. This comprehensive synthesis of the current evidence available provides an empirical foundation upon which further progress can be built by identifying priority areas requiring additional research efforts to fully address challenges on the responsible integration of suitable solutions for the commercial aquaculture industry globally.

Strategic Management of Eco-Innovation and Emerging Technologies for Sustainability in Agro-Based Industries

Eco-innovation and emerging technologies are increasingly central to driving sustainability in agro-based industries. This study examines the impact of technological advancements on sustainable agricultural practices, focusing on innovations that reduce environmental impacts while enhancing productivity. According to the USDA, precision agriculture systems implemented in the Midwest show a 15-20% reduction in fertilizer use and a 10-15% increase in crop yields. Furthermore, the adoption of renewable energy solutions, such as solar-powered irrigation in India, has led to a 30% decrease in energy costs and a 25% reduction in greenhouse gas emissions (GHGs), as reported by the International Renewable Energy Agency (IRENA). Biotechnology applications, such as genetically modified drought-resistant crops, have reduced pesticide use by up to 37% while increasing crop resilience, according to a study published in Nature. This paper also explores the socio-environmental implications of these technologies, emphasizing the need for balanced development that integrates technological progress with ecological sustainability. Case studies from Brazil, India, and Kenya provide further insights into the successful implementation of these innovations. The findings suggest that leveraging emerging technologies in agro-based industries can significantly enhance sustainability, contributing to a more resilient and equitable human-environment relationship.

Potential assessment of coordinated regulation of power load of emerging industrial users based on extreme scenarios of electric vehicle aggregators

AbstractWith the advancement of industrial low carbonization and electrification, emerging industrial production technologies have the characteristics of high‐power consumption and significant impact load. In the context of increasing global climate change, frequent extreme weather events have brought serious challenges to the balance of power and electricity in the power system, and have a significant impact on the production scheduling of industrial users, especially industrial users using electrified production technology. First, based on the representative high‐power industrial users of hydrogen reduction steel plants and internet datacentres (IDC), this paper establishes a flexible resource scheduling optimization model for single industrial users, and considers the collaborative relationship between multi‐user flexible resources to establish a collaborative scheduling optimization model for industrial users. Then, considering the charging and discharging characteristics of electric vehicles (EV) in extreme scenarios, the redundant capacity of EVs is aggregated by EV aggregators and sold to industrial users, and a collaborative scheduling optimization model of EV aggregators and industrial users is established. Finally, the effectiveness of the proposed model and algorithm is verified by simulation analysis. Compared with the traditional industrial user production optimization, the proposed model can tap the potential of multi‐agent scheduling operation in extreme scenarios.

Digital Twin Technology and Social Sustainability: Implications for the Construction Industry

To date, a plethora of research has been published investigating the value of using Digital Twin (DT) technology in the construction industry. However, the contribution of DT technology to promoting social sustainability in the industry has largely been unexplored. Therefore, the current paper aims to address this gap by exploring the untapped potential of DT technology in advancing social sustainability within the construction industry. To this end, a comprehensive systematic literature review was conducted, which identified 298 relevant studies. These studies were subsequently analysed with respect to their use of DT technology in supporting social sustainability. The findings indicated that the studies contributed to 8 of the 17 UN Sustainable Development Goals (SDGs), with a strong focus on SDG11 (77 publications), followed by SDG3 and SDG9, with 58 and 48 studies, respectively, focusing on promoting health and well-being and fostering resilient infrastructure and innovation. Other contributions were identified for SDG13 (30 studies), SDG7 (27 studies), SDG12 (26 studies), SDG4 (21 studies), and SDG6 (11 studies), covering areas such as climate action, responsible consumption, affordable energy, quality education, and clean water and sanitation. This paper also proposes future research directions for advancing DT technology to further enhance social sustainability in the construction industry. These include (i) enhancing inclusivity and diversity, (ii) workforce safety and well-being, (iii) training and skill development, (iv) policy and regulatory support, and (v) cross-disciplinary collaboration.

Bernstein operational matrix of differentiation to analyze the conductive, convective, and radiative non-linear sublimation model with temperature-dependent internal heat generation

Theoretical modeling of solid-vapor phase change process, known as sublimation, is of much interest in the pharmaceutical industry and freeze-drying technology. While past theoretical studies on sublimation assume that the phase change material (PCM) remains in direct contact with the heat source or sink with no heat loss due to the surrounding temperature, in most practical scenarios, PCM experiences less heat on its surface due to the loss of some amount of heat in the surrounding environment. Considering the impact of heat radiation on the porous surface is essential for ensuring the precision of the phase change process. The present study reports a theoretical model of a sublimation problem where heat generation is in proportion to the local temperature and loss of heat due to thermal radiation occurs in the porous medium. In connection with thermal radiation, a convective boundary condition is assumed at the surface of the porous body. The solution of the non-linear mathematical model is determined numerically via the Bernstein operational matrix of differentiation method. It is found that temperature-dependent heat generation enhances the temperature within the porous medium and thus, the rate of sublimation increases, while it is delayed due to heat loss through the radiation.

Development of a Digital Twin Driven by a Deep Learning Model for Fault Diagnosis of Electro-Hydrostatic Actuators

The first quarter of the 21st century has witnessed many technological innovations in various sectors. Likewise, the COVID-19 pandemic triggered the acceleration of digital transformation in organizations driven by artificial intelligence and communication technologies in Industry 4.0 and Industry 5.0. Aiming at the construction of digital twins, virtual representations of a physical system allow real-time bidirectional communication. This will allow the monitoring of operations, identification of possible failures, and decision making based on technical evidence. In this study, a fault diagnosis solution is proposed, based on the construction of a digital twin, for a cloud-based Industrial Internet of Things (IIoT) system contemplating the control of electro-hydrostatic actuators (EHAs). The system was supported by a deep learning model using Long Short-Term Memory (LSTM) networks for an effective diagnostic approach. The implemented study considers data preparation and integration and system development and application to evaluate the performance against the fault diagnosis problem. According to the results obtained, positive results are shown in the construction of the digital twin using a deep learning model for the fault diagnosis problem of an active EHA-IIoT configuration.