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Data Intensive Industrial Asset Management
Data Intensive Industrial Asset Management
Knygos.lt klubas Knygos.lt nariams
210,11 €
-15%
Įprastai
247,19 €
  • Išsiųsime per 12–18 d.d.
This book presents a step by step Asset Health Management Optimization Approach Using Internet of Things (IoT). The authors provide a comprehensive study which includes the descriptive, diagnostic, predictive, and prescriptive analysis in detail. The presentation focuses on the challenges of the parameter selection, statistical data analysis, predictive algorithms, big data storage and selection, data pattern recognition, machine learning techniques, asset failure distribution estimation, relia…
  • Leidėjas:
  • ISBN-10: 3030359328
  • ISBN-13: 9783030359324
  • Formatas: 15.6 x 23.4 x 1.4 cm, minkšti viršeliai
  • Kalba: Anglų

Data Intensive Industrial Asset Management (el. knyga) (skaityta knyga) | knygos.lt

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This book presents a step by step Asset Health Management Optimization Approach Using Internet of Things (IoT). The authors provide a comprehensive study which includes the descriptive, diagnostic, predictive, and prescriptive analysis in detail. The presentation focuses on the challenges of the parameter selection, statistical data analysis, predictive algorithms, big data storage and selection, data pattern recognition, machine learning techniques, asset failure distribution estimation, reliability and availability enhancement, condition based maintenance policy, failure detection, data driven optimization algorithm, and a multi-objective optimization approach, all of which can significantly enhance the reliability and availability of the system.

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  • Autorius: Farhad Balali
  • Leidėjas:
  • ISBN-10: 3030359328
  • ISBN-13: 9783030359324
  • Formatas: 15.6 x 23.4 x 1.4 cm, minkšti viršeliai
  • Kalba: Anglų

This book presents a step by step Asset Health Management Optimization Approach Using Internet of Things (IoT). The authors provide a comprehensive study which includes the descriptive, diagnostic, predictive, and prescriptive analysis in detail. The presentation focuses on the challenges of the parameter selection, statistical data analysis, predictive algorithms, big data storage and selection, data pattern recognition, machine learning techniques, asset failure distribution estimation, reliability and availability enhancement, condition based maintenance policy, failure detection, data driven optimization algorithm, and a multi-objective optimization approach, all of which can significantly enhance the reliability and availability of the system.

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