9789819631520 - data-driven fault diagnosis for complex industrial processes: towards fault prediction, detection and identification: 22 (engineering applications of computational methods, 22) de yin, hongpeng; zhou, han; chai, yi; tang, qiu (9 resultados)

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Editorial: Springer Nature Singapore, Springer Nature Singapore, 2025
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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book summarizes techniques of fault prediction, detection, and identification, all included specifically in the data-driven fault diagnosis requirements within industrial processes, drawing from the combination of data science, machine learning, and…domain-specific expertise. In the modern industrial processes, where efficiency, productivity, and safety stand as paramount pillars, the pursuit of fault diagnosis has become more crucial than ever. The widespread use of computer systems, along with new sensor hardware, generates significant quantities of real-time process data. It has been frequently asked what could be done with both the real-time and archived historical data, to not only promising efficiency but providing prospect of a brighter, more resilient future. This book starts with the definition, related work, and open test-bed for industrial process fault diagnosis. Then, it presents several data-driven methods on fault prediction, fault detection, and fault diagnosis, with consideration of properties of industrial processes, such as varying operation modes, non-Gaussian, nonlinearity. It distills cutting-edge methodologies and insights which may inspire for industrial practitioners, researchers, and academicians alike.

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Idioma: Inglés
Editorial: Springer, Berlin, Springer Nature Singapore, National Natural Science Foundation Of China, Natural Science Foundation Of Chongqing Municipality, Springer, 2025
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book summarizes techniques of fault prediction, detection, and identification, all included specifically in the data-driven fault diagnosis requirements within industrial processes, drawing from the combination of data science, machin…e learning, and domain-specific expertise. In the modern industrial processes, where efficiency, productivity, and safety stand as paramount pillars, the pursuit of fault diagnosis has become more crucial than ever. The widespread use of computer systems, along with new sensor hardware, generates significant quantities of real-time process data. It has been frequently asked what could be done with both the real-time and archived historical data, to not only promising efficiency but providing prospect of a brighter, more resilient future. This book starts with the definition, related work, and open test-bed for industrial process fault diagnosis. Then, it presents several data-driven methods on fault prediction, fault detection, and fault diagnosis, with consideration of properties of industrial processes, such as varying operation modes, non-Gaussian, nonlinearity. It distills cutting-edge methodologies and insights which may inspire for industrial practitioners, researchers, and academicians alike. 208 pp. Englisch.

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Buch. Condición: Neu. Data-Driven Fault Diagnosis for Complex Industrial Processes | Towards Fault Prediction, Detection and Identification | Hongpeng Yin (u. a.) | Buch | Engineering Applications of Computational Methods | xiv | Englisch | 2025 | Springer | EAN 9789819631520 | Verantwortliche Person für die EU: Springer Verlag…GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.

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Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book summarizes techniques of fault prediction, detection, and identification, all included specifically in the data-driven fault diagnosis requirements within industrial processes, drawing from the combination of data science, machine le…arning, and domain-specific expertise. In the modern industrial processes, where efficiency, productivity, and safety stand as paramount pillars, the pursuit of fault diagnosis has become more crucial than ever. The widespread use of computer systems, along with new sensor hardware, generates significant quantities of real-time process data. It has been frequently asked what could be done with both the real-time and archived historical data, to not only promising efficiency but providing prospect of a brighter, more resilient future. This book starts with the definition, related work, and open test-bed for industrial process fault diagnosis. Then, it presents several data-driven methods on fault prediction (Part I), fault detection (Part II), and fault diagnosis (Part III), with consideration of properties of industrial processes, such as varying operation modes, non-Gaussian, nonlinearity. It distills cutting-edge methodologies and insights which may inspire for industrial practitioners, researchers, and academicians alike.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 224 pp. Englisch.

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