| 書目名稱 | Prognostics and Health Management of Engineering Systems | | 副標(biāo)題 | An Introduction | | 編輯 | Nam-Ho Kim,Dawn An,Joo-Ho Choi | | 視頻video | http://file.papertrans.cn/760/759822/759822.mp4 | | 概述 | Explains how to determine the health status of the system using sensors and to predict the maintenance period.Systematically summarizes the current state-of-the-art in prognostics and health managemen | | 圖書封面 |  | | 描述 | This book introduces the methods for predicting the future behavior of a system’s health and the remaining useful life to determine an appropriate maintenance schedule. The authors introduce the history, industrial applications, algorithms, and benefits and challenges of PHM (Prognostics and Health Management) to help readers understand this highly interdisciplinary engineering approach that incorporates sensing technologies, physics of failure, machine learning, modern statistics, and reliability engineering. It is ideal for beginners because it introduces various prognostics algorithms and explains their attributes, pros and cons in terms of model definition, model parameter estimation, and ability to handle noise and bias in data, allowing readers to select the appropriate methods for their fields of application..Among the many topics discussed in-depth are:.? Prognostics tutorials using least-squares.? Bayesian inference and parameter estimation? Physics-based prognostics algorithms including nonlinear least squares, Bayesian method, and particle filter.? Data-driven prognostics algorithms including Gaussian process regression and neural network.? Comparison of different progn | | 出版日期 | Book 2017 | | 關(guān)鍵詞 | Bayesian Estimation; Condition-based Maintenance; Data-driven Prognostics Algorithms; Fatigue Damage in | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-319-44742-1 | | isbn_softcover | 978-3-319-83126-8 | | isbn_ebook | 978-3-319-44742-1 | | copyright | Springer International Publishing Switzerland 2017 |
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