Unmanned Full Processing Platforms; Using Subsea Technology as Enabler

This study uses a wind turbine case study as a subdomain of Industrial Internet of Things (IIoT) to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real-time condition monitoring, predictive analytics, and health management of selected components of wind turbines in a wind farm. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing, and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by machine learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as condition monitoring and predictive maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real-time sensor data and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations.

Digital twin applications in radiology and radiotherapy: Applications, challenges, and future perspectives.

The digitalization of industrial maintenance has transformed traditional approaches to asset management, particularly in the electrical domain. The integration of smart indicators, predictive analytics, and decision support systems has enabled industries to move from reactive and preventive models toward predictive and prescriptive maintenance strategies. This paradigm shift not only enhances operational efficiency but also extends equipment lifetime, reduces downtime, and strengthens safety and energy management. The purpose of this paper is to analyze how digital tools and intelligent indicators reshape the management of industrial utilities, emphasizing the interplay between predictive maintenance, data analytics, and decision-making. By reviewing recent academic literature, the article demonstrates that the successful adoption of smart indicators requires both robust technical architectures and human-centered decision support frameworks, highlighting challenges and opportunities for future advancements in the field.

<div class="section abstract"><div class="htmlview paragraph">To facilitate the construction of a robust transport infrastructure, it is essential to implement a digital transformation of the current highway system. The concept of digital twins, which are virtual replicas of physical assets, offers a novel approach to enhancing the operational efficiency and predictive maintenance capabilities of highway networks. The present study begins with an exhaustive examination of the demand for the smart highway digital twin model, underscoring the necessity for a comprehensive framework that addresses the multifaceted aspects of digital transformation. The framework, as proposed, is composed of six integral components: spatiotemporal data acquisition and processing, multidimensional model development, model integration, application layer construction, model iteration, and model governance. Each element is critical in ensuring the fidelity and utility of the digital twin, which must accurately reflect the dynamic nature of highway systems. The methodology for constructing a smart highway digital twin model is explored through a systematic approach that encompasses three pivotal stages. The first stage involves the comprehensive perception of spatiotemporal data, the foundation for any digital twin. The second stage pertains to entity modeling, where the physical assets of the highway system are digitized, thus creating a virtual representation that can be manipulated and analyzed. The final stage is real-time state modeling, which enables the digital twin to simulate the current state of the highway system, thereby providing real-time feedback and predictive analytics. This work aims to contribute to the theoretical and technical discourse surrounding smart highway digital twins, offering insights that can inform the development and practical application of such models. By adhering to the proposed framework and methodology, workers in the transportation sector can leverage the potential of digital twins to enhance safety, efficiency, and sustainability within the highway infrastructure ecosystem.</div></div>

Digital Twin (DT) technology is revolutionizing industry in the Industry 4.0 era by enabling simulating, analyzing, and monitoring physical assets real-time. To reduce downtime for machines, improve maintenance schedules, and maximize equipment lifespan, this research explores the use of digital twins in implementing predictive maintenance (PdM) concepts. An imitation of an actual machine or process that is continuously supplied with information from embedded Internet of Things sensors is referred to as a "digital twin." Condition-driven maintenance, in contrast to reactive or time-based maintenance, is enabled by the system to predict failures before they occur by the integration of AI/ML algorithms. With reference to four key components—data collection, model building, simulation environment, and feedback mechanisms—the research provides a conceptual framework for the application of digital twins in a typical manufacturing environment. It also illustrates a case study of a CNC machine that employs real-time temperature and vibration data to anticipate spindle bearing issues. Compared to standard methods, DT-based predictive maintenance enhances asset availability, cost savings, and accuracy in maintenance significantly. To deliver insights into future directions and scalability of DT in smart factories, the article concludes by considering challenges such as data security, interoperability, and computing complexity. Keywords: Industry 4.0, IoT, Smart Manufacturing, Machine Learning, CNC, Condition Monitoring, Digital Twin, Predictive Maintenance.

The rapid advancement of Industry 4.0 and the Internet of Things (IoT) has underscored the importance of effective diagnostics for electric motors, which are critical components in industrial, energy, and transportation systems. Traditional diagnostic methods, such as vibration, thermographic, and electromagnetic analyses, often lack adaptability and pre-dictive capabilities, limiting their ability to proactively identify faults under variable operating conditions. The digital twin concept, a virtual representation of physical assets synchronized with real-time sensor data, offers a transformative approach to technical condition monitoring. This paper reviews the state of diagnostic methods for electric mo-tors and evaluates the integration of digital twins to enhance diagnostic accuracy and pre-dictive maintenance. By leveraging platforms like Ansys Twin Builder and MATLAB/Simulink, digital twins enable real-time simulation, fault detection, and opera-tional optimization. The study proposes a novel methodology for diagnosing electric motor conditions using digital twins, incorporating mathematical modeling and real-time signal processing. Key findings include improved fault detection accuracy (MAE < 2.5%, RMSE < 3.1%) and the ability to simulate typical defects like rotor imbalance and insula-tion breakdown. The results demonstrate the potential of digital twins to revolutionize condition-based and predictive maintenance, offering scalable solutions for industrial applications. Future prospects include integrating machine learning for adaptive diagnos-tics and expanding digital twin applications to complex electromechanical systems.

In modern industrial systems, unplanned downtime leads to significant operational and financial losses. To address this, we propose a unified predictive maintenance pipeline that integrates generative anomaly injection, explainability, and reinforcement learning (RL) within a digital twin framework. Our system uses sensor data from real-world datasets (XJTU-SY, AI4I, and C-MAPSS) for Remaining Useful Life (RUL) prediction, fault simulation, and proactive maintenance policy generation. First, a GAN-based fault simulation module injects rare but realistic failure patterns using low discriminator confidence. Next, SHAP-based explainability is applied for feature attribution and drift monitoring. LSTM and Prophet models are employed to forecast RUL trends, which are fed into a Q-learning-based RL scheduler to optimize prescriptive actions like maintenance or load reduction. These actions are sent to a virtual command layer and closed-loop feedback is achieved via MQTT/OPC-UA to simulate interaction with the physical twin. We quantitatively evaluate the synthetic fault realism using t-SNE, PCA, and classifier generalization. The overall pipeline improves prediction accuracy by 12.6% and reduces failure downtime by 19.4% on average. SHAP analysis reveals interpretable patterns across synthetic and real failures. Our modular architecture demonstrates a self-optimizing, explainable, and simulation-augmented predictive maintenance system for Industry 4.0 environments. This work highlights the practical synergy of generative models, RL, and digital twins for scalable, real-time industrial reliability solutions.

Data Analytics and Artificial Intelligence for Predictive Maintenance in Industry 4.0 unites data science, machine learning, IIOT, and AI to enable predictive and prescriptive maintenance across manufacturing, energy, transportation, agriculture, and healthcare. With contributions from leading academics and practitioners, the book bridges foundational principles with cutting-edge industrial case studies ranging from digital twins and anomaly detection to federated learning and secure healthcare analytics. Key Features Explains fundamental concepts of data analytics, AI, and machine learning for predictive maintenance. Integrates IIoT, digital twins, federated learning, and blockchain into industrial maintenance strategies. Demonstrates real-world applications across manufacturing, energy, healthcare, and agriculture sectors. Analyzes optimization techniques, anomaly detection, condition monitoring, and RUL prediction models. Addresses security and ethical issues, including hardware protection and homomorphic encryption for healthcare. Maps future trends and emerging technologies driving predictive maintenance research.

The concept of the digital twin can be thought of as a virtual representation of a physical product, engineering system or facility. This paper presents the role of predictive engineering analytics, alongside operating data, in the digital twin. Using case studies, the authors demonstrate how predictive approaches can be developed to provide data where it cannot be measured and predict future operating data to improve performance, life and integrity of equipment, systems and facilities. The digital twin is fundamentally based on data, larger datasets provide greater insight. Sensor and inspection data are critical. However, there are scenarios in which engineers require data where it cannot be measured or requires data that cannot be measured. Engineers require ways to extract this data, this can be done through predictive engineering analytics. Predictive engineering analytics, in the form of science-based simulations, combines multiple approaches, often based on fundamental principles of physics and engineering. This paper will demonstrate how high-fidelity approaches, such as Finite Element Analysis (FEA) and Computational Fluid Dynamics (CFD), combined with system-level simulation and reduced-order modelling can work together with field data to provide this data in real-time. Two case studies presented show how combinations of different levels of science-based modelling approaches can help. A subsea thermal digital twin demonstrates how high-fidelity simulations, undertaken during system design, can be the foundation for reduced order system models capable of capturing critical thermal performance in real time; aiding hydrate risk management during operation. The methods used to train the real-time predictive approach are demonstrated. The second case presented focuses on structural integrity of a heat exchanger showing how real-time sensor data can be translated into structural integrity data and insight through simulation. The cases presented demonstrate the value of science-based predictions to generate data that cannot be obtained from operational sensor data alone. The authors aim to show how the predictive element of the digital twin can be first generated during design and evolved into real-time predictive approaches that provide operations with data that cannot be gained from sensors; when and where it is needed. Digital twins typically use physical data, limiting operators of in-field equipment to make operational decisions based on information from sensor locations and historical data alone. This can limit assessment of operational performance and integrity of complex production and process systems. In this paper the authors aim to show how, by combining predictive approaches, at differing levels of fidelity and based on fundamental scientific principles, it is possible to generate the missing data required, in both space and time. This can inform and guide operations; filling the gaps where and when physical data is not available.

A digital twin-based framework is developed in this paper for road condition monitoring and predictive maintenance, aiming to tackle the inefficiencies of traditional road inspection methods and the practical dilemmas in the operation of complex road infrastructure systems. Road maintenance management is confronted with prominent challenges including the spatial complexity of road networks, the nonlinear evolution of pavement damage, and the heterogeneity of multi-source monitoring data. To solve these practical problems, this research designs a three-layer digital twin architecture composed of physical space, data interaction space and virtual space, and puts forward three targeted innovative design strategies. First, autonomous inspection vehicles integrated with a multi-sensor system are deployed to realize continuous and real-time data acquisition under normal traffic conditions, without interrupting the regular road operation. Second, a unified data integration platform is constructed to standardize the format of heterogeneous monitoring data, thereby improving the interoperability and data sharing efficiency of different road monitoring systems. Third, a robotic maintenance system is designed to support targeted and automated repair operations, with maintenance decisions generated based on the predictive analysis results of the digital twin model. The case test results show that the proposed digital twin framework can significantly improve the efficiency of road condition inspection and provide reliable technical support for the formulation of predictive maintenance strategies for road infrastructure. Overall, the framework developed in this study provides a practical approach for improving road maintenance using digital twin technology. It shows potential for supporting more efficient and scalable maintenance processes in real-world applications.

Digital Twin (DT) technology, when combined with Artificial Intelligence (AI), has emerged as a transformative approach for predictive maintenance in modern industries. This paper synthesizes secondary evidence from academic literature, industry reports, and international standards (2018–2025) to examine how AI-enhanced digital twins are enabling predictive maintenance across manufacturing, energy, and transportation. Digital twins replicate physical assets virtually, while AI models analyze real-time sensor data to detect anomalies, forecast failures, and optimize maintenance schedules. Using structured analysis of ISO standards, Gartner, McKinsey, and IEEE literature, we map how digital twins integrate with IoT data streams, machine learning models, and maintenance workflows. The study identifies key enabling technologies (IoT, cloud computing, ML/DL), common architectural layers, and documented benefits, such as reduction in unplanned downtime (30–50%) and improved asset life cycles. A conceptual flowchart illustrates the AI–DT predictive maintenance loop, and a table compares sectoral adoption patterns. Findings highlight the convergence between AI analytics and DT simulations, enabling data-driven, condition-based maintenance strategies. Challenges remain in data interoperability, model updating, and cybersecurity. Future work should focus on standardized frameworks and cost-benefit models for broader adoption.

The concept of Industry 4.0 uses cyber-physical systems and the Internet of Things to create "smart factories" that enable automated and connected production. However, the complex and diverse nature of cyber-physical systems makes them vulnerable to physical threats, where any fault in their physical components can lead to serious consequences, from financial losses to human safety. To reduce the risk of such failures, it is essential to have techniques in place to monitor the condition of these physical components closely. In recent years, data-driven predictive maintenance methods have become common due to the growth in data and advancements in machine learning. This thesis focuses on fault diagnosis, a key part of predictive maintenance, which aims to detect and identify faults in physical components. Incorporating advanced techniques, including machine learning algorithms and sensor data analysis, enhanced fault diagnosis systems' accuracy and efficiency. However, several real-world barriers challenge the effectiveness of these techniques. This research is structured around three primary objectives. The first objective aims to establish an efficient fault diagnosis system that is highly sensitive to fault occurrences while being resilient to uncertainties such as variability in fault patterns and data imbalance. To address variability in fault patterns, we developed a multi-step approach integrating clustering techniques and an adaptive neuro-fuzzy inference system, which significantly improved diagnosis robustness even under noisy conditions. For imbalanced datasets, a two-step method was introduced, combining permutation entropy for fault detection and envelope analysis for fault isolation. This approach was enhanced by an automatic frequency band selection method leveraging wavelet transform and statistical criteria, resulting in high fault diagnosis accuracy and reliability, even with limited labeled faulty data. The second objective focuses on developing fault diagnosis systems that can maintain high performance despite domain shifts and limited training data in both source and target domains. To address the challenge of domain shifts, a transfer learning technique was introduced, and a systematic literature review was conducted. This study bridges a technical gap by providing a comprehensive taxonomy, state-of-the-art methods, and insights into the challenges of applying transfer learning to predictive maintenance. Additionally, a conceptual framework was proposed to explore scenarios for transferring diagnostic knowledge from different source domains to target domains with insufficient training datasets. In a subsequent study, a new diagnostic framework was developed using digital twins and partial transfer learning to address the lack of real training datasets in both source and target domains as well as label space inconsistencies between them. The framework includes a general approach for developing digital twins of real machines to generate synthetic diagnostic data as source domain data. Furthermore, by incorporating a deep learning network and a double-layer attention mechanism, a partial transfer learning diagnostic model was developed to transfer diagnostic knowledge from the digital twin to the real target domain despite label space inconsistencies. The proposed framework was validated through an industrial case study involving rolling bearings in rotating equipment. The third objective focuses on developing an efficient fault diagnosis system capable of maintaining reliability under unseen and evolving working conditions. To achieve this, a novel self-adaptive fault diagnosis system was proposed, incorporating the MAPE-K loop, a domain generalization network model, and digital twin technologies. This self-adaptive fault diagnosis system leverages the MAPE-K loop to adapt in real time to evolving working conditions. Digital twins were employed to continuously generate data aligned with the real system’s operational context, enabling accurate fault diagnosis in dynamic environments. The domain generalization network model enhances generalization to unseen target working conditions by addressing overfitting and feature loss through multi-domain data augmentation, adversarial learning, and domain discrepancy modules. Validation using multiple rotating machinery datasets demonstrated that the developed fault diagnosis system outperformed state-of-the-art methods across various scenarios. In summary, this dissertation proposes a comprehensive and practical approach to addressing variability in fault patterns, imbalance datasets, data scarcity, domain shifts, and unseen and evolving working conditions challenges in existing fault diagnosis systems.

Digital Twins, sophisticated digital replicas of physical entities, have been gaining significant attention, especially after NASA's endorsement, and are poised to revolutionize numerous fields, such as medicine and construction. These advanced models offer dynamic, real-time simulations, leveraging enabling technologies, such as artificial intelligence, machine learning, IoT, cloud computing, and Big Data analytics to enhance their functionality and applicability. In the medical field, Digital Twins facilitate personalized treatment plans and predictive maintenance of medical equipment by simulating human organs with precision. In construction, they enable efficient building design and urban planning, optimizing resource use, and reducing costs through predictive maintenance. Startups are innovatively employing Digital Twins in various sectors, from smart cities—where they optimize traffic flow, energy consumption, and waste management—to industrial machinery, ensuring predictive maintenance and minimizing downtime. This survey delves into the diverse use cases, market potential, and challenges of Digital Twins, such as data security and interoperability, while emphasizing their transformative impact on industries. The future prospects are promising, with continuous advancements in AI, ML, IoT, and cloud computing driving further expansion and application of Digital Twin technologies.

Predictive maintenance (PdM), supported by artificial intelligence (AI) and digital twin methods, is gaining attention as a practical and cost-efficient way to manage power generation assets. In the renewable energy sector, where performance, stability, and cost control are central concerns, PdM enables operators to anticipate equipment faults, schedule interventions more effectively, and reduce unplanned downtime. This paper reviews how such approaches are being applied in four different national contexts: China, Germany, Norway, and the Netherlands, and considers their contribution to cleaner and more reliable energy systems. The discussion highlights several patterns that emerge across these countries. In China, the rapid expansion of wind and solar capacity has driven the use of PdM to improve fault detection and optimize turbine and panel performance. Germany demonstrates how PdM can be integrated into broader energy transition policies, using digital twins and AI to balance fluctuating renewable output with grid demands. Norway shows the value of predictive tools in extending the life and efficiency of hydropower equipment, while the Netherlands illustrates the benefits of PdM in offshore wind projects, where remote monitoring and early fault recognition are critical. Evidence from these cases points to three consistent outcomes: improved uptime of renewable assets, measurable reductions in maintenance costs, and smoother integration of intermittent power sources through more advanced grid management. Taken together, these findings suggest that PdM is not only a set of technical tools but also a strategic component in building sustainable, resilient, and economically viable energy systems. Its wider adoption may help accelerate the transition toward low-carbon power on a global scale.

In the realm of proactive medical care, digital twin technology is rising to the forefront as a paradigm shift due to the combination of customised healthcare, 6G connectivity, and artificial intelligence (AI). This study investigates AI-driven, 6G-powered healthcare digital twin technology, focusing on patient-centric monitoring and predictive analytics. It must clearly outline the research objectives, such as real-time health data integration, adaptive decision-making, and improved treatment personalization. The proposed methodology should describe the use of 6G-enabled ultra-reliable low-latency communication, AI models for digital twin construction, and secure data management frameworks. Improved diagnostic accuracy, fast response time to clinical emergencies, and improved resource allocation in healthcare systems were main outcomes that are presented in this abstract. The technical contributions of this work with the integration of AI, 6G, and digital twin technologies are highlighted in the end of this study, and this may support the next-generation intelligent healthcare solutions. For self-learning healthcare digital twin, a model that leverages 6G-enabled medical informatics, real-time data streams, and artificial intelligence (AI) analytics is presented in this study. This presented model may support in clinical diagnosis, and classifying damages earlier. A real-time updated digital copy of the patient was created by the suggested method by integrating multimodal data from various sources including imaging, vital signs, consumer electronic devices such as smartwatches, wearable sensor metrics, and electronic health records (EHRs). The patient physiological patterns and previous injury profiles are constantly learnt by the integration of digital twin and deep learning (DL) algorithms. This integration is effective in predicting the occurrence, kind, and severity of injuries and internal trauma, falls, cardiovascular events, and neurodegenerative insults. The digital twin and healthcare providers are connected seamlessly with 6G communication infrastructure, guaranteeing ultra-low latency and great dependability. This allows for pre-hospital and critical care settings to implement instantaneous feedback loops, as well as for remote diagnostics and adaptive clinical decision-making. From home health monitoring solutions to smartphones and wearables, consumer electronics are ubiquitous and serve as common data collectors, greatly increase access and enabling the instant collection of accurate data using a digital twin. To keep data private while training AI models on dispersed healthcare data, the architecture offers federated learning. Based on severity level, the injury types are classified by a multi-layered decision engine, and this may promote a customized, context-aware responses like alerting emergency teams, modifying treatment regimens, or demanding robotic surgical support. The AI-driven, 6G-powered healthcare digital twin revolutionizes the diagnosis of injury, and it establish a path for a precise, preventive, and predictive medicine. Furthermore, scaling efficiencies were reduced by 95.81%, while reaction times decreased to 96.88%, resulting in significant gains. This technology is particularly helpful for populations who are elderly, at risk, or located far away, as it decreases reaction time, reduces diagnostic error, and provides scalable, continuous care.