Machine Learning and Knowledge Discovery for Engineering Systems Health Management (Hardcover)

Ashok N. Srivastava, Jiawei Han

  • 出版商: CRC
  • 出版日期: 2011-11-16
  • 售價: $3,600
  • 貴賓價: 9.5$3,420
  • 語言: 英文
  • 頁數: 502
  • 裝訂: Hardcover
  • ISBN: 1439841780
  • ISBN-13: 9781439841785
  • 相關分類: Machine Learning
  • 立即出貨 (庫存=1)

商品描述

Machine Learning and Knowledge Discovery for Engineering Systems Health Management presents state-of-the-art tools and techniques for automatically detecting, diagnosing, and predicting the effects of adverse events in an engineered system. With contributions from many top authorities on the subject, this volume is the first to bring together the two areas of machine learning and systems health management.

Divided into three parts, the book explains how the fundamental algorithms and methods of both physics-based and data-driven approaches effectively address systems health management. The first part of the text describes data-driven methods for anomaly detection, diagnosis, and prognosis of massive data streams and associated performance metrics. It also illustrates the analysis of text reports using novel machine learning approaches that help detect and discriminate between failure modes. The second part focuses on physics-based methods for diagnostics and prognostics, exploring how these methods adapt to observed data. It covers physics-based, data-driven, and hybrid approaches to studying damage propagation and prognostics in composite materials and solid rocket motors. The third part discusses the use of machine learning and physics-based approaches in distributed data centers, aircraft engines, and embedded real-time software systems.

Reflecting the interdisciplinary nature of the field, this book shows how various machine learning and knowledge discovery techniques are used in the analysis of complex engineering systems. It emphasizes the importance of these techniques in managing the intricate interactions within and between the systems to maintain a high degree of reliability.

商品描述(中文翻譯)

《機器學習與知識發現在工程系統健康管理中的應用》介紹了自動檢測、診斷和預測工程系統不良事件影響的最新工具和技術。本書由許多頂尖專家共同貢獻,是首次將機器學習和系統健康管理這兩個領域結合在一起。

本書分為三個部分,解釋了基於物理和基於數據的方法如何有效地應對系統健康管理。第一部分介紹了用於異常檢測、診斷和預測大量數據流和相關性能指標的數據驅動方法。它還展示了使用新型機器學習方法分析文本報告,幫助檢測和區分故障模式。第二部分重點介紹了基於物理的診斷和預測方法,探討這些方法如何適應觀察到的數據。它涵蓋了在複合材料和固體火箭發動機中研究損傷傳播和預測的基於物理、數據驅動和混合方法。第三部分討論了機器學習和基於物理方法在分布式數據中心、飛機發動機和嵌入式實時軟件系統中的應用。

本書反映了這一領域的跨學科性質,展示了各種機器學習和知識發現技術在分析複雜工程系統中的應用。它強調了這些技術在管理系統內部和系統之間的複雜相互作用以保持高度可靠性的重要性。