期刊检索

  • 2019年第51卷
  • 2018年第50卷
  • 2017年第49卷
  • 2016年第48卷
  • 2015年第47卷
  • 2014年第46卷
  • 2013年第45卷
  • 2012年第44卷
  • 2011年第43卷
  • 2010年第42卷
  • 第1期
  • 第2期

主管单位 中华人民共和国
工业和信息化部
主办单位 哈尔滨工业大学 主编 冷劲松 国际刊号ISSN 0367-6234 国内刊号CN 23-1235/T

期刊网站二维码
微信公众号二维码
引用本文:谭治学,钟诗胜,林琳.民航发动机性能诊断方法[J].哈尔滨工业大学学报,2019,51(1):22-28.DOI:10.11918/j.issn.0367-6234.201801117
TAN Zhixue,ZHONG Shisheng,LIN Lin.Method for performance diagnosis of commercial aero-engine[J].Journal of Harbin Institute of Technology,2019,51(1):22-28.DOI:10.11918/j.issn.0367-6234.201801117
【打印本页】   【HTML】   【下载PDF全文】   查看/发表评论  下载PDF阅读器  关闭
过刊浏览    高级检索
本文已被:浏览 144次   下载 229 本文二维码信息
码上扫一扫!
分享到: 微信 更多
民航发动机性能诊断方法
谭治学,钟诗胜,林琳
(哈尔滨工业大学 机电学院,哈尔滨 150001)
摘要:
为利用民航发动机工作状态参数对其部件衰退情况进行在线诊断,提出一种发动机稳态建模和无迹卡尔曼滤波(unscented Kalman flter, UKF)相结合的部件性能衰退诊断方法. 用多元非线性函数分别表达各个部件的通用特性曲线,采用机器学习方法对实测发动机工作状态参数进行学习以确定各函数系数,进而获得发动机稳态工作模型;在对各部件的性能衰退特点及影响性分析的基础上,定义出具有代表性的部件性能衰退因子;对无迹卡尔曼滤波器进行改造,采用所获得的发动机稳态工作模型和工况参数替换传统的滤波观测方程,并以新提出的滑动窗口采样策略克服低可观测性问题;对发动机实测运行数据进行滤波,得出各部件的性能衰退因子变化趋势. 经发动机实际运营监控数据验证,该方法能够在航段数据返回数据中心后快速诊断出发动机各部件性能状态,诊断结果与发动机部件损伤目视检查信息吻合良好. 该方法能够有效克服发动机性能诊断过程中的非线性强、观测性低、数据噪声显著的问题,具有较高的实用价值.
关键词:  航空发动机  性能诊断  发动机稳态建模  发动机部件  无迹卡尔曼滤波
DOI:10.11918/j.issn.0367-6234.201801117
分类号:V235.13
文献标识码:A
基金项目:国家自然科学基金重点项目(U1533202); 民航科技项目重大专项(MHRD20150104); 山东省自主创新及成果转化专项(2014CGZH1101)
Method for performance diagnosis of commercial aero-engine
TAN Zhixue,ZHONG Shisheng,LIN Lin
(School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China)
Abstract:
For using online data to diagnose the degradation status of commercial aero-engine components, an aggregated method including engine steady state modeling and unscented Kalman filter (UKF) was proposed. First, the method represents the general characteristic curves of components with nonlinear multivariate functions with respect to their working conditions, and uses machine learning methods to confirm function coefficients that fit the observed data best, subsequently acquires the steady working model of aero-engines. Then, by analysis of individual degradation properties and effects on entire engine performance of components, the key degradation factors of components were defined. Moreover, the standard UKF process was modified, by adding moving window sampling strategy to overcome low-observability problem, and replacing standard observation equation with combined model of engine steady state model and engine working conditions. At last, the acquired filter was used to process observed engine data to track the trends of component degradation factors. As indicated by the test on commercial engine dataset, the proposed method can conveniently diagnose the health status of engine components once the observed report returns to on-ground data centers, and the acquired results coincided well with the internal damage information of engines collected by visual inspection. The proposed method performed well in overcoming modeling nonlinearity, low-observability, and high data noise problems in aero-engine performance diagnosis and showed to be applicable to industrial conditions.
Key words:  aero-engine  performance diagnosis  engine steady state modeling  engine components  unscented Kalman filter

友情链接LINKS