Dragan Komljenovic – författare

Key insights and practical guidance on transitioning to clean energy while meeting increasing energy demands, covering AI developments and more Prognostics and Health Management in Energy and Power Systems explores two highly topical subjects, energy transition and the latest advances in Artificial Intelligence, and provides insights and practical guidance for a smooth transition to clean, low-carbon energy while simultaneously continuing to meet the ever-increasing demand for energy. The first part of this book is completely devoted to the challenges, trends, and Asset Management requirements for the energy transition and explains why the energy system of the future must be resilient, autonomous, anticipatory, and situation-aware. The second part of the book presents key developments in recent years and shows the gradual shift from a collection of monolithic architectures for narrow, singular tasks to a set of modular, reconfigurable architectures capable of handling different types of tasks. An industrial case study is illustrated in the third part of the book, showing that Large-Scale Foundation models represent a promising technique to support the Prognostics and Health Management of the energy system. This book includes information on: Key differences between reliability and resilience, covering Low-Impact, High-Probability events and High-Impact, Low-Frequency eventsImportant factors in the operation of current and future power plants and substations, including software, complexity, human error, data, and maintenanceModularity, reliability, and explainability of Large-Scale Foundation modelsTransformer-based Deep Neural Networks, covering Attention Mechanisms, Positional Encoding, and input-output data embeddingGraph-based approaches to prognostics of complex machinery with sparse Run-to-Failure data, covering diagnostics feature extraction and graph dataset generationPrognostics and Health Management in Energy and Power Systems is an essential forward-thinking reference for engineers and researchers working in the energy sector with an interest in AI techniques and Machine Learning.

Key insights and practical guidance on transitioning to clean energy while meeting increasing energy demands, covering AI developments and more Prognostics and Health Management in Energy and Power Systems explores two highly topical subjects, energy transition and the latest advances in Artificial Intelligence, and provides insights and practical guidance for a smooth transition to clean, low-carbon energy while simultaneously continuing to meet the ever-increasing demand for energy. The first part of this book is completely devoted to the challenges, trends, and Asset Management requirements for the energy transition and explains why the energy system of the future must be resilient, autonomous, anticipatory, and situation-aware. The second part of the book presents key developments in recent years and shows the gradual shift from a collection of monolithic architectures for narrow, singular tasks to a set of modular, reconfigurable architectures capable of handling different types of tasks. An industrial case study is illustrated in the third part of the book, showing that Large-Scale Foundation models represent a promising technique to support the Prognostics and Health Management of the energy system. This book includes information on: Key differences between reliability and resilience, covering Low-Impact, High-Probability events and High-Impact, Low-Frequency events Important factors in the operation of current and future power plants and substations, including software, complexity, human error, data, and maintenance Modularity, reliability, and explainability of Large-Scale Foundation models Transformer-based Deep Neural Networks, covering Attention Mechanisms, Positional Encoding, and input-output data embedding Graph-based approaches to prognostics of complex machinery with sparse Run-to-Failure data, covering diagnostics feature extraction and graph dataset generation Prognostics and Health Management in Energy and Power Systems is an essential forward-thinking reference for engineers and researchers working in the energy sector with an interest in AI techniques and Machine Learning.

Key insights and practical guidance on transitioning to clean energy while meeting increasing energy demands, covering AI developments and more Prognostics and Health Management in Energy and Power Systems explores two highly topical subjects, energy transition and the latest advances in Artificial Intelligence, and provides insights and practical guidance for a smooth transition to clean, low-carbon energy while simultaneously continuing to meet the ever-increasing demand for energy. The first part of this book is completely devoted to the challenges, trends, and Asset Management requirements for the energy transition and explains why the energy system of the future must be resilient, autonomous, anticipatory, and situation-aware. The second part of the book presents key developments in recent years and shows the gradual shift from a collection of monolithic architectures for narrow, singular tasks to a set of modular, reconfigurable architectures capable of handling different types of tasks. An industrial case study is illustrated in the third part of the book, showing that Large-Scale Foundation models represent a promising technique to support the Prognostics and Health Management of the energy system. This book includes information on: Key differences between reliability and resilience, covering Low-Impact, High-Probability events and High-Impact, Low-Frequency events Important factors in the operation of current and future power plants and substations, including software, complexity, human error, data, and maintenance Modularity, reliability, and explainability of Large-Scale Foundation models Transformer-based Deep Neural Networks, covering Attention Mechanisms, Positional Encoding, and input-output data embedding Graph-based approaches to prognostics of complex machinery with sparse Run-to-Failure data, covering diagnostics feature extraction and graph dataset generation Prognostics and Health Management in Energy and Power Systems is an essential forward-thinking reference for engineers and researchers working in the energy sector with an interest in AI techniques and Machine Learning.

This book gathers selected peer-reviewed papers from the 14th World Congress on Engineering Asset Management (WCEAM), which was held in Singapore on 28–31 July 2019, as well as papers presented during the 1st WCEAMOnline event which focused on the ramifications of Covid-19 on infrastructure systems.This book covers a wide range of topics in engineering asset management, including:asset management services provisioning;servitization;decision-making;asset management systems;industrial Internet of things; andvulnerability and resilience of infrastructure systems. The breadth and depth of these state-of-the-art, comprehensive proceedings make them an excellent resource for asset management practitioners, researchers and academics, as well as undergraduate and postgraduate students.

This book gathers selected peer-reviewed papers from the 14th World Congress on Engineering Asset Management (WCEAM), which was held in Singapore on 28–31 July 2019, as well as papers presented during the 1st WCEAMOnline event which focused on the ramifications of Covid-19 on infrastructure systems.This book covers a wide range of topics in engineering asset management, including:asset management services provisioning;servitization;decision-making;asset management systems;industrial Internet of things; andvulnerability and resilience of infrastructure systems. The breadth and depth of these state-of-the-art, comprehensive proceedings make them an excellent resource for asset management practitioners, researchers and academics, as well as undergraduate and postgraduate students.

This book gathers selected peer-reviewed papers from the 14th World Congress on Engineering Asset Management (WCEAM), which was held in Singapore on 28–31 July 2019, as well as papers presented during the 1st WCEAMOnline event which focused on the ramifications of Covid-19 on infrastructure systems.This book covers a wide range of topics in engineering asset management, including:asset management services provisioning;servitization;decision-making;asset management systems;industrial Internet of things; andvulnerability and resilience of infrastructure systems. The breadth and depth of these state-of-the-art, comprehensive proceedings make them an excellent resource for asset management practitioners, researchers and academics, as well as undergraduate and postgraduate students.

17th WCEAM Proceedings provides a record of some of the intellectual discussions (including keynote addresses, research paper presentations, panel debates and practical workshops) that took place among the attendees and participants of the 17th World Congress on Engineering Asset Management (WCEAM), held from 18 - 20 October 2023 at the Sheraton Saigon Hotel and Towers, Ho Chi Minh City, Vietnam. The events were organized by the International Society for Engineering Asset Management (ISEAM) and hosted by RMIT University Vietnam LLC (RMIT VN), Ho Chi Ming City. The content of the book includes topics listed below under a general theme of Sustainable Management of Engineered Assets in a Post-Covid World: Industry 4.0, Digital Transformation, Society 5.0 and beyondSustainable asset investment, acquisition, operations, maintenance, and retirement strategiesProduction-service transformation and product-service systemsSustainable asset acquisition, operations, maintenance, and retirement processesModeling and simulation of acquisition, operations, maintenance, and retirement processesReliability and resilience engineeringApplications of the Fourth Industrial Revolution (4IR) technologies in EAM, e.g., Digital TwinsCybersecurity issues in asset managementAsset condition, risk, resilience, and vulnerability assessmentsAsset management and decision support systemsApplications of international and logical guidelines and standards in EAMHuman dimensions and asset management performanceCase studies of asset management in various industries and sectorsThis proceedings is an excellent resource for asset management practitioners, researchers and academics, as well as undergraduate and postgraduate students.