9781439841785 - machine learning and knowledge discovery for engineering systems health management (13 risultati)
Lingua: Inglese
Editore: Chapman and Hall/CRC, 2011
Serie: Chapman & Hall/CRC Data Mining and Knowledge Discovery, Libro 40 di 56. Libro 40 di 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
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Lingua: Inglese
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Serie: Chapman & Hall/CRC Data Mining and Knowledge Discovery, Libro 40 di 56. Libro 40 di 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
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Editore: Taylor & Francis Inc, Washington, 2011
Serie: Chapman & Hall/CRC Data Mining and Knowledge Discovery, Libro 40 di 56. Libro 40 di 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
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Hardcover. Condizione: new. Hardcover. Machine Learning and Knowledge Discovery for Engineering Systems Health Management presents state-of-the-art tools and techniques for automatically detecting, diagnosing, and predicting the effects of adverse events in an engineered system. With contributions from many top authorities on th…e subject, this volume is the first to bring together the two areas of machine learning and systems health management. Divided into three parts, the book explains how the fundamental algorithms and methods of both physics-based and data-driven approaches effectively address systems health management. The first part of the text describes data-driven methods for anomaly detection, diagnosis, and prognosis of massive data streams and associated performance metrics. It also illustrates the analysis of text reports using novel machine learning approaches that help detect and discriminate between failure modes. The second part focuses on physics-based methods for diagnostics and prognostics, exploring how these methods adapt to observed data. It covers physics-based, data-driven, and hybrid approaches to studying damage propagation and prognostics in composite materials and solid rocket motors.The third part discusses the use of machine learning and physics-based approaches in distributed data centers, aircraft engines, and embedded real-time software systems. Reflecting the interdisciplinary nature of the field, this book shows how various machine learning and knowledge discovery techniques are used in the analysis of complex engineering systems. It emphasizes the importance of these techniques in managing the intricate interactions within and between the systems to maintain a high degree of reliability. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Lingua: Inglese
Editore: Chapman and Hall/CRC, 2011
Serie: Chapman & Hall/CRC Data Mining and Knowledge Discovery, Libro 40 di 56. Libro 40 di 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
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Serie: Chapman & Hall/CRC Data Mining and Knowledge Discovery, Libro 40 di 56. Libro 40 di 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
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Condizione: New. pp. 502 Index.
Lingua: Inglese
Editore: Taylor & Francis Group, 2011
Serie: Chapman & Hall/CRC Data Mining and Knowledge Discovery, Libro 40 di 56. Libro 40 di 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
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Lingua: Inglese
Editore: Chapman & Hall, 2011
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Hardcover. Condizione: Brand New. 1st edition. 502 pages. 9.25x6.25x1.25 inches. In Stock.
Lingua: Inglese
Editore: Taylor & Francis Inc, Washington, 2011
Serie: Chapman & Hall/CRC Data Mining and Knowledge Discovery, Libro 40 di 56. Libro 40 di 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
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Hardcover. Condizione: new. Hardcover. Machine Learning and Knowledge Discovery for Engineering Systems Health Management presents state-of-the-art tools and techniques for automatically detecting, diagnosing, and predicting the effects of adverse events in an engineered system. With contributions from many top authorities on th…e subject, this volume is the first to bring together the two areas of machine learning and systems health management. Divided into three parts, the book explains how the fundamental algorithms and methods of both physics-based and data-driven approaches effectively address systems health management. The first part of the text describes data-driven methods for anomaly detection, diagnosis, and prognosis of massive data streams and associated performance metrics. It also illustrates the analysis of text reports using novel machine learning approaches that help detect and discriminate between failure modes. The second part focuses on physics-based methods for diagnostics and prognostics, exploring how these methods adapt to observed data. It covers physics-based, data-driven, and hybrid approaches to studying damage propagation and prognostics in composite materials and solid rocket motors.The third part discusses the use of machine learning and physics-based approaches in distributed data centers, aircraft engines, and embedded real-time software systems. Reflecting the interdisciplinary nature of the field, this book shows how various machine learning and knowledge discovery techniques are used in the analysis of complex engineering systems. It emphasizes the importance of these techniques in managing the intricate interactions within and between the systems to maintain a high degree of reliability. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Lingua: Inglese
Editore: Taylor & Francis Group, 2011
Serie: Chapman & Hall/CRC Data Mining and Knowledge Discovery, Libro 40 di 56. Libro 40 di 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
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Condizione: New. pp. 502 243 This item is printed on demand.
Lingua: Inglese
Editore: CRC Press, 2011
Serie: Chapman & Hall/CRC Data Mining and Knowledge Discovery, Libro 40 di 56. Libro 40 di 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
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Gebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Ashok N. Srivastava is the Principal Scientist for Data Mining and Systems Health Management at NASA. Dr. Srivastava has received many awards, including the IEEE Computer Society Technical Achievement Award…, the NASA Exceptional Achievem.
