{"product_id":"prognostics-and-health-management-in-energy-and-power-systems-integrating-situation-awareness-into-large-scale-foundation-models-hardback-9781394366996","title":"Prognostics and Health Management in Energy and Power Systems; Integrating Situation Awareness into Large-Scale Foundation Models (Hardback) 9781394366996","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003ePrognostics and Health Management in Energy and Power Systems\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eIntegrating Situation Awareness into Large-Scale Foundation Models\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eRyad M. Zemouri (Author), Jean Raymond (Author), Dragan Komljenovic (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394366996, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 26 January 2026\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e256 pages\u003cbr\u003e25.4 x 18 x 2 cm, 0.612 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eKey insights and practical guidance on transitioning to clean energy while meeting increasing energy demands, covering AI developments and more\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003ePrognostics and Health Management in Energy and Power Systems\u003c\/i\u003e 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. \u003c\/p\u003e\n\u003cp\u003eThe 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. \u003c\/p\u003e\n\u003cp\u003eThis book includes information on: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e Key differences between reliability and resilience, covering Low-Impact, High-Probability events and High-Impact, Low-Frequency events\u003c\/li\u003e\n\u003cli\u003e Important factors in the operation of current and future power plants and substations, including software, complexity, human error, data, and maintenance\u003c\/li\u003e\n\u003cli\u003e Modularity, reliability, and explainability of Large-Scale Foundation models\u003c\/li\u003e\n\u003cli\u003e Transformer-based Deep Neural Networks, covering Attention Mechanisms, Positional Encoding, and input-output data embedding\u003c\/li\u003e\n\u003cli\u003e Graph-based approaches to prognostics of complex machinery with sparse Run-to-Failure data, covering diagnostics feature extraction and graph dataset generation\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003ePrognostics and Health Management in Energy and Power Systems\u003c\/i\u003e is an essential forward-thinking reference for engineers and researchers working in the energy sector with an interest in AI techniques and Machine Learning.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eList of Figures xi\u003c\/p\u003e \u003cp\u003eList of Tables xvii\u003c\/p\u003e \u003cp\u003eAbstract xix\u003c\/p\u003e \u003cp\u003eAbout the Authors xxi\u003c\/p\u003e \u003cp\u003ePreface xxiii\u003c\/p\u003e \u003cp\u003eAcknowledgments xxv\u003c\/p\u003e \u003cp\u003eNotations xxvii\u003c\/p\u003e \u003cp\u003eAbout the Companion Website xxix\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 The Energy Transition: Toward a Highly Interconnected System of Systems 1\u003c\/p\u003e \u003cp\u003e1.2 The Power Plant and Substation of the Future: Toward Situational Awareness 2\u003c\/p\u003e \u003cp\u003e1.3 The New Paradigm in AI: The Emergence of the Large-scale Foundation Models 3\u003c\/p\u003e \u003cp\u003e1.4 Topics and Organization of the Book 4\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Challenges, Trends, and Asset Management Requirements for the Energy Transition 7\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Energy Transition and Digital Transformation 9\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 9\u003c\/p\u003e \u003cp\u003e2.2 Digital Transformation 11\u003c\/p\u003e \u003cp\u003e2.3 Energy Transition 12\u003c\/p\u003e \u003cp\u003e2.4 Arrival of DERs 13\u003c\/p\u003e \u003cp\u003e2.5 Lifecycle Requirements, Expectations, and Speed of New Technologies, Introduction in the Electric System 14\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Asset Management and Resilience 15\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 15\u003c\/p\u003e \u003cp\u003e3.2 Asset Management 15\u003c\/p\u003e \u003cp\u003e3.3 Resilience 17\u003c\/p\u003e \u003cp\u003e3.4 Combining AM and Resilience: Resilience-based AM 18\u003c\/p\u003e \u003cp\u003e3.5 Key Differences Between Reliability and Resilience 20\u003c\/p\u003e \u003cp\u003e3.6 The Link Between DTs, Reliability, LCM, and AM 21\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Challenges and Issues Surrounding the Operation of Current and Future Power Plants and Substations 25\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 25\u003c\/p\u003e \u003cp\u003e4.2 Reliability and Asset Management 27\u003c\/p\u003e \u003cp\u003e4.3 Different Designs 29\u003c\/p\u003e \u003cp\u003e4.4 Sensor Proliferation 29\u003c\/p\u003e \u003cp\u003e4.5 Dynamic Systems 29\u003c\/p\u003e \u003cp\u003e4.6 Cohabitation of Current and New-generation Technologies 29\u003c\/p\u003e \u003cp\u003e4.7 Software 30\u003c\/p\u003e \u003cp\u003e4.8 Complexity 32\u003c\/p\u003e \u003cp\u003e4.9 Behavioral Nonlinearity of Components and Systems 35\u003c\/p\u003e \u003cp\u003e4.10 System of Systems 35\u003c\/p\u003e \u003cp\u003e4.11 Human Factors 36\u003c\/p\u003e \u003cp\u003e4.12 Data 37\u003c\/p\u003e \u003cp\u003e4.13 Different Operational Time Ranges of the Electric Network 37\u003c\/p\u003e \u003cp\u003e4.14 Possible Multistates of a Component 38\u003c\/p\u003e \u003cp\u003e4.15 Maintenance 38\u003c\/p\u003e \u003cp\u003e4.16 Hidden Failures 39\u003c\/p\u003e \u003cp\u003e4.17 Degradation Process and Obsolescence of Electric and Mechanical Components or Systems 40\u003c\/p\u003e \u003cp\u003e4.18 Climate Change, Extreme Weather Events, and Others 40\u003c\/p\u003e \u003cp\u003e4.19 Complete Life Cycle of Component\/System 43\u003c\/p\u003e \u003cp\u003e4.20 Prescriptive Maintenance or Knowledge-based Maintenance 43\u003c\/p\u003e \u003cp\u003e4.21 Regulation Evolution 44\u003c\/p\u003e \u003cp\u003e4.22 Prosumers 45\u003c\/p\u003e \u003cp\u003e4.23 Potential Consequences of Energy Transition 46\u003c\/p\u003e \u003cp\u003e4.24 Remaining Technical Gaps for Electric Power Utilities 47\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Large-scale Foundation Models 51\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 From Shallow Machine Learning to the Requirements of Large-scale Foundation Models 53\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 53\u003c\/p\u003e \u003cp\u003e5.2 ANNs: Theoretical Foundations 54\u003c\/p\u003e \u003cp\u003e5.3 A Brief History of AI: The Main Developments 57\u003c\/p\u003e \u003cp\u003e5.4 Trustworthiness of AI Systems 61\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Main Elements of Large-scale Foundation Models: Theoretical Backgrounds 77\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 77\u003c\/p\u003e \u003cp\u003e6.2 Modular Learning 78\u003c\/p\u003e \u003cp\u003e6.3 Transformer-based DNNs 82\u003c\/p\u003e \u003cp\u003e6.4 Self-supervised Learning 87\u003c\/p\u003e \u003cp\u003e6.5 Multimodal Fusion 90\u003c\/p\u003e \u003cp\u003e6.6 Multitask Learning 93\u003c\/p\u003e \u003cp\u003e6.7 Graph-oriented Approaches 93\u003c\/p\u003e \u003cp\u003e6.8 Conclusion 99\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Main Elements of Large-scale Foundation Models: A Practical and Literature Review 101\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 101\u003c\/p\u003e \u003cp\u003e7.2 Transformer Architecture-based Deep Neural Network 101\u003c\/p\u003e \u003cp\u003e7.3 Self-supervised Learning 104\u003c\/p\u003e \u003cp\u003e7.4 Multimodal Fusion 107\u003c\/p\u003e \u003cp\u003e7.5 Multitask Learning 109\u003c\/p\u003e \u003cp\u003e7.6 Graph-oriented Approaches 110\u003c\/p\u003e \u003cp\u003e7.6.1 Anomaly Detection 114\u003c\/p\u003e \u003cp\u003e7.6.2 Diagnostics 114\u003c\/p\u003e \u003cp\u003e7.6.3 Prognostics 114\u003c\/p\u003e \u003cp\u003e7.7 Conclusion and Synthesis 116\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Combining Situational Awareness and LSF Models to Support the Energy Transition 119\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 119\u003c\/p\u003e \u003cp\u003e8.2 The Target of Future Power Plants and Substations 120\u003c\/p\u003e \u003cp\u003e8.3 What Is the Situational Awareness? 121\u003c\/p\u003e \u003cp\u003e8.4 Incorporating the SA to the Power Plant\/Substation of the Future 122\u003c\/p\u003e \u003cp\u003e8.5 Conclusion 124\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Toward a New PHM Process 125\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 The Concept of PHM Process 125\u003c\/p\u003e \u003cp\u003e9.2 Integrating ML into the PHM Process 126\u003c\/p\u003e \u003cp\u003e9.3 The Situational Awareness Integrated to the PHM Process 128\u003c\/p\u003e \u003cp\u003e9.4 Conclusion 130\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Industrial Case Study 131\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Hydro-generators Prognostics and Health Management 133\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 133\u003c\/p\u003e \u003cp\u003e10.2 Description of the Case Study 133\u003c\/p\u003e \u003cp\u003e10.3 Overview of the Global Methodology 142\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Set of Deep Learning Models for Feature Extraction 145\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 145\u003c\/p\u003e \u003cp\u003e11.2 Feature Extraction from Visual Inspection Data 145\u003c\/p\u003e \u003cp\u003e11.3 Feature Extraction from Text Data 149\u003c\/p\u003e \u003cp\u003e11.4 Feature Extraction from PD 152\u003c\/p\u003e \u003cp\u003e11.5 Conclusion 155\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Set of AI-Experts with Deep Modular Learning 157\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 157\u003c\/p\u003e \u003cp\u003e12.2 Description of the AI-Experts 158\u003c\/p\u003e \u003cp\u003e12.3 Managing the Mixture-of-AI-Experts 161\u003c\/p\u003e \u003cp\u003e12.4 Experimental Results 164\u003c\/p\u003e \u003cp\u003e12.5 Conclusion 169\u003c\/p\u003e \u003cp\u003e12.6 Appendix 169\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Graph-based Approach for Prognostics of Complex Machinery with Sparse Run-to-failure Data 175\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 175\u003c\/p\u003e \u003cp\u003e13.2 Preliminaries and Assumptions 176\u003c\/p\u003e \u003cp\u003e13.3 Diagnostics Feature Extraction 177\u003c\/p\u003e \u003cp\u003e13.4 Graph Structure Definition 178\u003c\/p\u003e \u003cp\u003e13.5 Graph Dataset Generation for the Prognostics Considering the Sparse RTF Data 179\u003c\/p\u003e \u003cp\u003e13.6 Assigning a Likelihood for Each Edge 180\u003c\/p\u003e \u003cp\u003e13.7 Graph-based Forecasting Model 181\u003c\/p\u003e \u003cp\u003e13.8 Experimental Results 184\u003c\/p\u003e \u003cp\u003e13.9 Conclusion 190\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV Conclusion 191\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Conclusion 193\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 What to Keep in Mind 193\u003c\/p\u003e \u003cp\u003e14.2 Future Directions 195\u003c\/p\u003e \u003cp\u003eAcronyms 199\u003c\/p\u003e \u003cp\u003eGlossary 203\u003c\/p\u003e \u003cp\u003eReferences 205\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Brand New","offer_id":52433825104152,"sku":"9781394366996","price":101.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394366996.jpg?v=1784854228","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/prognostics-and-health-management-in-energy-and-power-systems-integrating-situation-awareness-into-large-scale-foundation-models-hardback-9781394366996","provider":"Freshly Printed Books","version":"1.0","type":"link"}