Accurate and fast machine learning algorithm for systems outage prediction

The frequency of extreme events (e.g., hurricanes, earthquakes, and floods) and man-made attacks (cyber and physical attacks) has increased dramatically in recent years. These events have severely impacted power systems ranging from long outage times to major equipment (e.g., substations, transmission lines, and power plants) destructions. This calls for developing control and operation methods and planning strategies to improve grid resilience against such events. The first step toward this goal is to develop resilience metrics and evaluation methods to compare planning and operation alternatives and to provide techno-economic justifications for resilience enhancement. Although several power system resilience definitions, metrics, and evaluation methods have been proposed in the literature, they have not been universally accepted or standardized. This paper provides a comprehensive and critical review of current practices of power system resilience metrics and evaluation methods and discusses future directions and recommendations to contribute to the development of universally accepted and standardized definitions, metrics, evaluation methods, and enhancement strategies. This paper thoroughly examines the consensus on the power system resilience concept provided by different organizations and scholars and existing and currently practiced resilience enhancement methods. Research gaps, associated challenges, and potential solutions to existing limitations are also provided.

This study examines the potential of digital twin (DT) technology to strengthen urban security, with a specific focus on flood risk management in smart cities. A DT is understood as a virtual representation of real-world assets and processes, continuously synchronised with data from the physical environment. Building on an analysis of the existing DT literature and maturity assessment, identified operational requirements and the authors’ expertise in crisis management, this study proposes a structured set of DT maturity levels with stage boundary conditions and illustrative measurable indications and designs a maturity-driven data content model for a flood-oriented DT. The framework identifies essential data layers, sensing requirements and integration mechanisms necessary for representing hydrological, infrastructural and environmental conditions at operationally meaningful update frequencies. This study further outlines the conceptual architecture of a flood DT and discusses its potential to support prediction, situational awareness and decision making across crisis management phases. By providing recommendations for DT implementation and highlighting opportunities for future development, this study contributes to ongoing efforts to enhance the resilience and safety of urban areas through advanced digital technologies.

Purpose: This research aims to analyze the performance and accuracy of machine learning algorithms in predicting heart disease, which is a cause of death throughout the world.Design/methodology/approach: The algorithms analyzed include Logistic Regression, Naive Bayes, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, XGBoost, and Neural Network. A publicly available dataset containing patients' medical records was utilized, with the methodology encompassing data collection, Exploratory Data Analysis (EDA), model training, and performance evaluation.Findings/result: The results indicate that the Random Forest algorithm achieved the highest accuracy with an accuracy of 90.16%, followed by Logistic Regression and Naive Bayes with accuracies of 85.25%. The K-Nearest Neighbors algorithm exhibits the lowest accuracy at 67.21%.Originality/value/state of the art: This research highlights the advantages of certain machine learning algorithms in predicting heart disease and contributes knowledge to early detection technology in the health sector.

Performances of machine learning algorithms for individual thermal comfort prediction based on data from professional and practical settings

We demonstrate how a visual digital twin system is used to implement a digital twin for pipeline monitoring. A visual digital twin allows for ingestion of data for calibration and creation of models which enable a digital replica of a real-world physical system for decision support and improving situational awareness. A laboratory experimental pipeline system is used to generate data to drive modelling capabilities in this system. We show how machine learning operations (MLOps) principles are applied in the context of digital twins, forming a sub-study area known as Digital Twin ML Ops (DT MLOps). The intended purpose of the system is to both train operators of the leak detection system in its use and provide high situational awareness and operational readiness to users. We demonstrate how multiple sources of monitoring data from an experimental pipeline setup, as well as simulation can be combined in a visual digital twin system and used for pipeline leak detection and leak plume and flow visual prediction. We show how leak detection and visual leak prediction are visualized in the context of a pipeline twin along with confidence and uncertainty and other explainable elements from machine learning models. We demonstrate how models are tracked using the DT MLOps sub system. The overall system demonstrates a novel combination of real experimental data driving models for both leak detection and the prediction of the associated leak plume and pipeline flow visual assessment imagery. This demonstrates a system which can detect leaks and their locations and also provide operators with assessment of the leak. The system provides provenance through its DT MLOps capabilities. This presented virtual pipeline and leak model, which integrates AI with experimental validation, is not widely available in industry.

Industrial control systems rely on alarm systems to alert operators to abnormal conditions. These systems frequently generate alarm floods, periods of intense alarm activities that overwhelm operators and compromise situational awareness. The root causes of alarm floods range from operational faults arising from equipment failures to sophisticated cyberattacks deliberately designed to manipulate process controls. Despite their appearance as periods of intense alarm activities, alarm floods resulting from operational faults and cyberattacks are fundamentally different. Recognition of this distinction is essential, as each category requires its own diagnostic approach and response protocols. This thesis addresses the dual challenge of classifying alarm floods and identifying their originating fault types. It also detects anomalous patterns indicative of previously unseen cyber intrusions. This research presents two complementary deep learning frameworks for alarm flood analysis. First, a convolutional neural network (CNN) architecture is developed for online alarm flood classification that transforms temporal alarm sequences into spatial-temporal heatmap representations. This visual pattern recognition system captures alarm activation patterns across process variables and time, enabling real-time fault classification. The framework incorporates multi-channel input encoding to distinguish valid alarm data from padded regions, progressive feature extraction through hierarchical convolutional layers, and demonstrates early classification capability using partial alarm information as floods evolve. Second, a class-conditioned autoencoder (C2AE) framework is introduced for open-set recognition, enabling the system to distinguish between known operational faults and previously unseen cyberattack patterns. C2AE employs a maximum separation training strategy that explicitly maximizes the reconstruction quality gap between matched and mismatched class conditions. This approach enables robust detection of cyberattacks as out-of-distribution samples through reconstruction-based anomaly scoring, while simultaneously classifying known faults. The framework addresses the critical limitation of closed-set classifiers that force unknown cyberattack patterns into known fault categories. Both methodologies are validated on the Tennessee Eastman Process benchmark, a widely adopted testbed for process control research. The CNN-based classifier achieves high accuracy in distinguishing between five operational fault scenarios, with classification performance improving as alarm floods evolve over time. The C2AE framework demonstrates effective open-set recognition by successfully identifying four types of cyber attacks as unknown patterns while maintaining accurate classification of known operational faults. Comprehensive evaluation includes closed-set fault classification performance, open-set cyber attack detection capabilities, and analysis of reconstruction error patterns that enable discrimination between legitimate faults and malicious intrusions. The proposed frameworks provide operators with enhanced situational awareness and enable appropriate emergency response strategies in safety-critical industrial environments facing both operational disturbances and evolving cyber security threats.

Operators of cyber-physical systems (CPSs) need to maintain awareness of the cyber situation in order to be able to adequately address potential issues in a timely manner. For instance, detecting early symptoms of cyber attacks may speed up the incident response process and mitigate consequences of attacks (e.g., business interruption, safety hazards). However, attaining a full understanding of the cyber situation may be challenging, given the complexity of CPSs and the ever-changing threat landscape. In particular, CPSs typically need to be continuously operational, may be sensitive to active scanning, and often provide only limited in-depth analysis capabilities. To address these challenges, we propose to utilize the concept of digital twins for enhancing cyber situational awareness. Digital twins, i.e., virtual replicas of systems, can run in parallel to their physical counterparts and allow deep inspection of their behavior without the risk of disrupting operational technology services. This paper reports our work in progress to develop a cyber situational awareness framework based on digital twins that provides a profound, holistic, and current view on the cyber situation that CPSs are in. More specifically, we present a prototype that provides real-time visualization features (i.e., system topology, program variables of devices) and enables a thorough, repeatable investigation process on a logic and network level. A brief explanation of technological use cases and outlook on future development efforts completes this work.

This paper presents an autonomous drilling advisory system powered by digital twins and AI solutions. Such an advisory system aims to automate real-time monitoring and parameter optimization, reduce subject-matter experts, and meet the demands for safer and more efficient drilling toward autonomous operation. The methodology proposed in this research involves the creation of a comprehensive Digital Twin model that accurately replicates the drilling process by integrating hydraulic, thermal dynamic, and mechanical models. To ensure high model accuracy, an auto-calibration approach is developed, driven by real-time data, to fine-tune the Digital Twin models. Additionally, AI-based model reasoning techniques are employed to detect potential hazards and risks ahead of the bit proactively. This is achieved by comparing the ideal behavior of the digital twin replica with the actual behavior observed from downhole and the rig. As a result, real-time diagnostics are generated to supervise ongoing operations, accompanied by suggestions to mitigate identified risks. Furthermore, the system leverages the capabilities of the Digital Twin and optimization methods to create multiple combinations of operational parameters. These parameters are optimized by ranking the predicted performance derived from the Digital Twin. The optimized operational parameters are automatically generated as forward advice to drillers, enabling them to make informed decisions and enhance drilling performance. Testing results on multiple wells from different operators are presented, showcasing the system's capabilities in real-time monitoring and drilling parameter optimization. The system demonstrates its effectiveness in providing diagnostic messages with early anomaly detection during drilling and casing running. These diagnostic warnings include losses, leakage, poor hole cleaning, and stuck pipe, enabling proactive intervention to mitigate risks. Furthermore, the system optimizes operational parameters during drilling and tripping in real-time without requiring human intervention. This optimization covers parameters such as flow rate, rotary speed (RPM), and rate of penetration (ROP) during drilling, and tripping speed during tripping in and pulling out of the hole. The time savings achieved through the use of optimized parameters are quantified for both cases, demonstrating a substantial improvement in operational efficiency while maintaining safety margins. The scalability and adaptability of the system are also highlighted, emphasizing its ability to accommodate diverse drilling scenarios and integrate with existing solutions in various deployment conditions. The proposed methodology demonstrates the development of a robust and efficient system that enhances decision-making and improves drilling performance. In addition, the results highlight the potential benefits of combining AI and Digital Twin technologies in the drilling industry, paving the way for future innovations and advancements in the field.

Controlling Unmanned Aerial Vehicles (UAVs) is a cognitively demanding task, with accidents often arising from insufficient situational awareness, inadequate training, and bad user experiences. Providing more intuitive and immersive visual feedback—particularly through Digital Twin technologies—offers new opportunities to enhance pilot awareness and the overall experience quality. In this study, we investigate how different virtual points of view (POVs) influence user experience and performance during UAV piloting in Virtual Reality (VR), utilizing a digital twin that faithfully replicates the real-world flight environment. We developed a VR application that enables participants to control a physical DJI Mini 4 Pro drone while immersed in a digital twin with four distinct camera perspectives: Baseline View (static external), First Person View, Chase View, and Third Person View. Nineteen participants completed a series of ring-based obstacle courses from each perspective. In addition to objective flight data, we collected standardized subjective assessments of user experience, presence, workload, cybersickness, and situational awareness. Quantitative analyses revealed that the First Person View was associated with significantly higher mental demand and effort, greater trajectory deviation, but smoother control inputs compared to the Third Person and Chase perspectives. Complementing these findings, preference data indicated that the Third Person View was most consistently favored, whereas the First Person View elicited polarized reactions.

Modelling cyber resilience in a water treatment and distribution system

Offering efficacious instruction for production based technical education to provide adequate hands-on training has always been a growing concern in technical universities. This paper proposes the use of Digital Twins as a tool for training students on production based systems and justifies the proposition by instantiating learning theories within a pedagogical digital twin framework. Further, the paper investigates how situational awareness can be beneficial in such an environment and its contribution to the overall learning experience.

The growing interest in Digital Twin (DT) Technology represents a significant advancement in academic research and industrial applications. Leveraging advancements in Internet of Things (IoT), sensors, and communication devices, DTs are increasingly utilised across different sectors, notably in the energy domain such as Power Systems and Smart Grids. DT concepts facilitate the creation of virtual models mirroring physical assets, streamlining real-time data management and analysis. Driven by the potential of DTs to revolutionise energy systems, this paper offers a comprehensive review of DT applications in the power sector, specifically within next-generation energy systems like Smart Grids. TThe integration of DT technology with Machine Learning (ML) algorithms is highlighted as a key factor in significantly enhancing the performance and capabilities of these advanced energy systems. In contrast to prior reviews, our study meticulously investigates all of the crucial components of energy systems, including forecasting, anomaly detection, and security, which are fundamental for improving the management of operational grids. In addition, the study examines the seamless incorporation of Renewable Energy into current grids and investigates how DT technology could contribute to Electric Vehicles for increased sustainability and reliability within the Smart Grid framework. This review underlines that DTs significantly enhance the management of real-time data and analysis, consequently improving operational grid management. There are ample opportunities into further research and development to design a more advanced and digital system as compared to conventional power systems. The findings are presented in clear and concise tables, highlighting current limitations, proposing effective solutions, and identifying potential future research directions in academia and industry.

Studying human system integration provides critical guidelines for understanding how systems should be designed by and for humans. Those guidelines are even more essential in the design of complex systems such as Digital Twins (DTs). DTs are holistic models that aim to support humans in understanding complex systems. The oil-and-gas domain, aiming to ensure safety, studied how to design a DT of their installation to support control room operators better and increase safety. This study showed the necessity of understanding the users’ activity through cognitive function analysis and their situation awareness. Those analyses were performed with domain experts, providing the study with direct user insights. Those insights were used to apply recommended human–system integration (HSI) practices, such as human-in-the-loop tests and a simulator that ensured the user a specific place in the design process. The results enabled a system design that provides humans with on-point information to support their cognitive decision process. This approach also questioned classical technology-driven design, highlighting the need for specific information sources. It showed that HSI practices can be applied in the industry to support the design of systems according to human needs.

With the rapid development of information technology and the rapid popularization of the Internet, while people enjoy the convenience and efficiency brought about by new technologies, they are also suffering from the harm caused by cyber attacks. In addition to efficiently thwarting network assaults, a high volume of complicated security event data might unintentionally increase the strain of policy makers. At present, NS threats mainly include network viruses, trojans, DOS (Denial-Of-Service), etc. For the increasingly complex Network Security (NS) problems, the traditional rule-based network monitoring technology is difficult to predict the unknown attack behavior. Environment-based, dynamic and integrated data fusion can integrate data from a macro perspective. In recent years, Machine Learning (ML) technology has developed rapidly, which could easily train, test and predict existing third-party models. It uses ML algorithms to find out the association between data rather than manually sets rules. Support vector machine is a common ML method, which can predict the security of the network well after training and testing. In order to monitor the overall security status of the entire network, NS situation awareness refers to the real-time and accurate reproduction of network attacks using the reconstruction approach. Situation awareness technology is a powerful network monitoring and security technology, but there are many problems in the existing NS technology. For example, the state of the network cannot be accurately detected, and its change rule cannot be understood. In order to effectively predict network attacks, this paper adopted a technology based on ML and data analysis, and constructed a NS situational awareness model. The results showed that the detection efficiency of the model based on ML and data analysis was 7.18% higher than that of the traditional NS state awareness model.

Relevance. Increased competition in the conditions of the digital economy encourages enterprises to actively introduce the capabilities of modern information systems and technologies into management practice. To optimize management decision-making, they increasingly turn to digital twin tools, which allows managers to obtain more accurate insights into business processes, reducing risks. The relevance of introducing digital twins into the practice of economic activity of enterprises with the support of artificial intelligence is due to the high degree of efficiency and economic feasibility. Such modern digital technologies are considered one of the key tools of Digital Transformation 4.0 in the country's industry. The purpose is to study the possibility of transforming new-generation decision support systems by analyzing the practice of implementing digital twins with the integration of artificial intelligence tools to enhance the capabilities of digital models. Objectives. The main objective of the study is to analyze the practices of successful application of modern digital technologies and the integration of strategic digitalization initiatives with improved economic results, expressed in increased efficiency of operational processes and improved financial performance of the organization. Methodology. The general scientific approach, generalization, analysis and synthesis for an in-depth study of the research object were used as methods for achieving the goal of research activity. Results. For Russian companies, digital twins with the integration of artificial intelligence open up the possibility of reducing the impact of negative factors, filling the technological gap, and increasing competitiveness in the market. Conclusions. The implementation of modern digital technologies by enterprises with the support of artificial intelligence has proven its effectiveness and economic feasibility. Organizations that have integrated digital twins into their structure will have a competitive advantage in the accuracy of analytics and the speed of response to changes, which is especially important in the conditions of today's dynamic economy.