Principles of Gas Path Analysis
暫譯: 氣路分析原則

Volponi, Allan J., Tang, Liang

Principles of Gas Path Analysis
  • 出版商: CRC
  • 出版日期: 2025-12-11
  • 售價: $8,250
  • 貴賓價: 9.5 折 $7,838
  • 語言: 英文
  • 頁數: 422
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 1041092679
  • ISBN-13: 9781041092674
  • 相關分類: 控制系統 Control-systems

    • 海外代購書籍(需單獨結帳)

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商品描述

Principles of Gas Path Analysis offers a self-contained reference of the concept and theory of gas path analysis (GPA), as both a diagnostic and prognostic methodology for gas turbine engines. It provides a chronological account of the methodology as it evolved over the past 50 years.

Before expanding into specific GPA concepts, the book begins by covering the basics that are generic to diagnostics and prognostics as well as a discussion of engine health management (EHM) and how GPA can contribute to this strategy. The text further introduces essential parameter corrections important for understanding the foundational principles of GPA. Additionally, advanced topics such as information fusion and ambiguity resolution are explored to highlight potential future advancements in the field. Examples are provided using simulated data generated from a fictional high bypass turbofan engine model. The book contains a comprehensive set of appendices with detailed treatment of the mathematical derivations and statistics behind GPA as well as specialized constructs for relevant methods such as neural networks and fuzzy logic.

The book is intended for professional engineers engaged in the gas turbine industry and EHM, including aircraft operators and maintainers. It will also benefit researchers studying gas turbine engine diagnostics and prognostics.

商品描述(中文翻譯)

《氣體通道分析原則》提供了一個獨立的參考資料，涵蓋氣體通道分析（GPA）的概念和理論，作為燃氣渦輪引擎的診斷和預測方法論。它按時間順序記錄了這一方法論在過去50年中的演變過程。

在深入具體的GPA概念之前，本書首先介紹了與診斷和預測相關的基本知識，以及引擎健康管理（EHM）的討論，並說明GPA如何能夠為這一策略做出貢獻。文本進一步介紹了理解GPA基本原則所需的重要參數修正。此外，還探討了信息融合和模糊解決等進階主題，以突顯該領域未來可能的進展。書中提供了使用虛構的高旁通渦輪風扇引擎模型生成的模擬數據的示例。本書包含了一整套附錄，詳細處理了GPA背後的數學推導和統計，以及針對相關方法（如神經網絡和模糊邏輯）的專門構造。

本書旨在為從事燃氣渦輪行業和EHM的專業工程師提供參考，包括飛機操作員和維護人員。對於研究燃氣渦輪引擎診斷和預測的研究人員也將有所裨益。

作者簡介

Dr. Allan J Volponi is a retired Senior Fellow from the Controls & Diagnostic Systems organization at Pratt & Whitney in East Hartford, CT. He received his B.S. and M.S. degrees from Pratt Institute (1971-2), and Ph.D. from the Adelphi University (1977), all in mathematics. He spent 40 years with United Technologies, initially with the Hamilton Sundstrand Division as a Senior Principal Engineer in the engine controls group before transferring to P&W in 2000. His interests are in propulsion health management where he has been active in the development of engine performance diagnostic systems. Dr. Volponi has been the recipient of numerous awards including the 1992 Manly Memorial Medal by the SAE, the 2006 Aircraft Engine Technology Award by ASME, the Silver Specialist and Sir Roy Fedden Awards from the Royal Aeronautical Society, and the 2013 Scholar Award from ASME/IGTI. Dr. Volponi has been an active member of the International Gas Turbine Institute (IGTI) and a past Chairman of its Controls, Diagnostics and Instrumentation Committee and is an ASME Fellow. He holds 17 patents, has 2 pending, and he is the author of numerous technical papers on gas turbine diagnostics and prognostics health management.

Dr. Liang Tang is an expert in jet engine health management, controls, and the application of artificial intelligence (AI) and machine learning (ML) in aerospace. Since 2012, he has served in several technical leadership positions with two major engine OEMs, focusing on engine health management, diagnostics and prognostics, and the application of AI/ML to inspection and process improvements. Dr. Tang is recognized for extending gas path analysis from snapshot-based approaches to the full-flight domain and for applying ML techniques to resolve diagnostic ambiguity. Since 2005, he has also led multiple government-funded research programs as Principal Investigator for agencies including NASA and the DoD, spanning health management, controls, navigation, and autonomy. Dr. Tang earned his Ph.D. from Shanghai Jiao Tong University in 1999 and was a postdoctoral research fellow at the Georgia Institute of Technology from 2003 to 2004. He has published more than 60 papers and holds multiple patents related to diagnostics, prognostics and health management for aerospace systems. He remains actively engaged in technical committees and standards organizations such as the SAE and ASME, where he has held several leadership positions, including chairing technical committees over the past decade.

作者簡介(中文翻譯)

艾倫·J·沃爾波尼博士是位於康乃狄克州東哈特福的普惠公司（Pratt & Whitney）控制與診斷系統組織的退休高級研究員。他於1971年至1972年在普拉特學院（Pratt Institute）獲得學士和碩士學位，並於1977年在阿德爾菲大學（Adelphi University）獲得數學博士學位。他在聯合技術公司（United Technologies）工作了40年，最初在漢密爾頓·桑德斯特（Hamilton Sundstrand）部門擔任引擎控制組的高級首席工程師，並於2000年轉至普惠公司。他的興趣在於推進健康管理，並積極參與引擎性能診斷系統的開發。沃爾波尼博士獲得了多項獎項，包括1992年由SAE頒發的曼利紀念獎（Manly Memorial Medal）、2006年由ASME頒發的航空引擎技術獎（Aircraft Engine Technology Award）、英國皇家航空學會頒發的銀獎專家獎（Silver Specialist Award）和羅伊·費登爵士獎（Sir Roy Fedden Awards），以及2013年由ASME/IGTI頒發的學者獎（Scholar Award）。沃爾波尼博士是國際燃氣渦輪學會（IGTI）的活躍成員，曾擔任其控制、診斷與儀器委員會的主席，並且是ASME的會士。他擁有17項專利，還有2項正在申請中，並且是多篇有關燃氣渦輪診斷和預測健康管理的技術論文的作者。

唐亮博士是噴氣引擎健康管理、控制以及人工智慧（AI）和機器學習（ML）在航空航天應用方面的專家。自2012年以來，他在兩家主要引擎原始設備製造商（OEM）擔任多個技術領導職位，專注於引擎健康管理、診斷和預測，以及AI/ML在檢查和流程改進中的應用。唐博士因將氣流分析從基於快照的方法擴展到全飛行範疇而受到認可，並應用機器學習技術解決診斷模糊性。自2005年以來，他還作為主要研究員領導多個政府資助的研究計劃，涉及健康管理、控制、導航和自主性，合作機構包括NASA和國防部（DoD）。唐博士於1999年在上海交通大學獲得博士學位，並於2003年至2004年在喬治亞理工學院擔任博士後研究員。他已發表超過60篇論文，並擁有多項與航空航天系統的診斷、預測和健康管理相關的專利。他仍然積極參與技術委員會和標準組織，如SAE和ASME，並在過去十年中擔任多個領導職位，包括技術委員會的主席。

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