民用航空发动机故障诊断与健康管理现状、挑战与机遇Ⅰ:气路、机械和FADEC系统故障诊断与预测  

Current status,challenges and opportunities of civil aero-engine diagnostics&health managementⅠ:Diagnosis and prognosis of engine gas path,mechanical and FADEC

作　　者：曹明 黄金泉[3] 周健 陈雪峰[4] 鲁峰[3] 魏芳 CAO Ming;HUANG Jinquan;ZHOU Jian;CHEN Xuefeng;LU Feng;WEI Fang(AECC Commercial Aircraft Engine Co.,Ltd,Shanghai 201109,China;School of Aeronautics and Astronautics,Shanghai Jiao Tong University,Shanghai 200240,China;School of Energy&Power,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China;School of Mechanical Engineering,Xi’an Jiaotong University,Xi’an 710049,China)

机构地区：[1]中国航发商用航空发动机有限责任公司,上海201109 [2]上海交通大学航空航天学院,上海200240 [3]南京航空航天大学能源与动力学院,南京210016 [4]西安交通大学机械工程学院,西安710049

出　　处：《航空学报》2022年第9期1-33,共33页Acta Aeronautica et Astronautica Sinica

基　　金：国家科技重大专项(2017-Ⅳ-0008-0045)。

摘　　要：最近这一二十年相关工程技术的发展,给民用航空发动机故障诊断与健康管理(EHM)系统研发提出了新的挑战和机遇。本文综述围绕EHM偏上游功能的民用发动机气路性能退化诊断和预测、发动机机械系统故障和发动机FADEC系统故障诊断与3个模块的设计验证技术的需求、必要性及现状进行了讨论,并指出了未来的主要研发方向。全文的讨论围绕以下关键技术发展趋势展开:基于非线性无迹卡尔曼滤波器(UKF)和深度学习神经网络的发动机气路故障诊断算法己经显示出提高气路诊断精度的潜力;复合材料叶片在涡扇发动机里己经得到广泛使用;增材制造技术正被越来越多地应用于复杂发动机零部件的制造;金属屑末传感器的精度已获得大幅提高,其技术成熟度己达到发动机使用要求,为与振动信号的融合诊断铺平了道路;电气化、智能化的发动机全权限数字控制系统(FADEC)发展趋势对现有的基于传统构型控制部件和集中式控制架构的故障诊断算法也提出了新的挑战。The engineering advancements during the last two decades have presented opportunities as well as challenges for the Engine Health Management(EHM)system development of civil aero-engines.This R&D review provides an in-depth discussion on EHM needs,gaps and potential solutions/future R&D development directions,focusing on the“up-stream”EHM development modules:Engine gas path diagnostics and prognostics,mechanical diagnostics and prognostics,FADEC diagnostics and prognostics.Results shows the Unscented Kalman Filter(UKF)method and deep-learning neural networks have shown promises on improving the engine gas path diagnostics accuracy;composite fans have found widespread applications in turbo-fan engines;powder metallurgy has seen more and more applications on fabricating aero-engine parts with complex shapes;the accuracies of metal particle sensing technologies have witnessed significant improvements,with technology readiness level matching the aero-engine needs,and paved the way for fusion diagnostics with vibration signal.The result also show that electrification and intelligentization trends of FADEC system presents new challenges for the diagnostics of the traditionally centralized control architecture.

关 键 词：涡扇发动机 发动机健康管理系统 气路故障诊断与预测 发动机机械故障诊断与预测 FADEC诊断与预测 

分 类 号：V239[航空宇航科学与技术—航空宇航推进理论与工程]