Machine Learning and Knowledge Discovery for Engineering Systems Health Management (Hardcover)
暫譯: 工程系統健康管理的機器學習與知識發現 (精裝版)

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於工程系統健康管理》介紹了最先進的工具和技術,用於自動檢測、診斷和預測工程系統中不利事件的影響。本書匯集了許多該領域的頂尖專家,是首部將機器學習與系統健康管理兩個領域結合在一起的著作。

本書分為三個部分,解釋了物理基礎和數據驅動方法的基本算法和方法如何有效地解決系統健康管理問題。第一部分描述了用於異常檢測、診斷和預測大量數據流及相關性能指標的數據驅動方法。它還展示了使用新穎的機器學習方法分析文本報告,幫助檢測和區分故障模式。第二部分專注於診斷和預測的物理基礎方法,探討這些方法如何適應觀察到的數據。它涵蓋了物理基礎、數據驅動和混合方法在複合材料和固體火箭發動機中研究損傷擴展和預測的應用。第三部分討論了在分佈式數據中心、飛機發動機和嵌入式實時軟體系統中使用機器學習和物理基礎方法的情況。

本書反映了該領域的跨學科特性,展示了各種機器學習和知識發現技術在複雜工程系統分析中的應用。它強調了這些技術在管理系統內部及之間複雜互動的重要性,以維持高可靠性。