data driven fault diagnosis complex von yin hongpeng (10 Ergebnisse)

Hardcover. Zustand: new. Hardcover. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Zustand: New.

Buch. Zustand: 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.

Hardcover. Zustand: Brand New. 222 pages. 9.25x6.10x9.49 inches. In Stock.

Buch. Zustand: 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, 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. 208 pp. Englisch.

Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

Buch. Zustand: Neu. Data-Driven Fault Diagnosis for Complex Industrial Processes | Towards Fault Prediction, Detection and Identification | Hongpeng Yin (u. a.) | Buch | 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.

Buch. Zustand: 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 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 (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 GmbH, Tiergartenstr. 17, 69121 Heidelberg 224 pp. Englisch.

Zustand: New. Print on Demand.

Zustand: New. PRINT ON DEMAND.