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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.
| Autorius: | Farhad Balali, Tian Zhao, Adel Nasiri, Jessie Nouri, |
| Leidėjas: | Springer Nature Switzerland |
| Išleidimo metai: | 2021 |
| Knygos puslapių skaičius: | 260 |
| ISBN-10: | 3030359328 |
| ISBN-13: | 9783030359324 |
| Formatas: | 235 x 155 x 15 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Data Intensive Industrial Asset Management: IoT-based Algorithms and Implementation“
