基于改进D-S证据理论的航空发动机转子故障决策融合诊断研究

doi:10.7527/S1000-6893.2013.0313

流体力学与飞行力学

本期目录 | 过刊浏览 | 高级检索

前一篇 | 后一篇

基于改进D-S证据理论的航空发动机转子故障决策融合诊断研究

胡金海¹, 余治国², 翟旭升³, 彭靖波¹, 任立通¹

1. 空军工程大学航空航天工程学院, 陕西西安 710038;
2. 解放军驻四三零厂军事代表室, 陕西西安 710021;
3. 空军第一航空学院, 河南信阳 464000

收稿日期:2013-04-27 修回日期:2013-06-23 出版日期:2014-02-25 发布日期:2013-07-15
通讯作者: 胡金海，Tel.：029-84787506 E-mail：lh_hjh_78@163.com E-mail:lh_hjh_78@163.com
作者简介:胡金海男，博士，副教授。主要研究方向：航空发动机健康监控、故障诊断与综合控制。Tel：029-84787506 E-mail：lh_hjh_78@163.com
基金资助:
国家自然科学基金（51105374）

Research of Decision Fusion Diagnosis of Aero-engine Rotor Fault Based on Improved D-S Theory

HU Jinhai¹, YU Zhiguo², ZHAI Xusheng³, PENG Jingbo¹, REN Litong¹

1. Aeronautics and Astronautics Engineering Institute, Air Force Engineering University, Xi'an 710038, China;
2. Military Representative Office of PLA in Factory No. 430, Xi'an 710021, China;
3. The First Aeronautical College of Air Force, Xinyang 464000, China

Received:2013-04-27 Revised:2013-06-23 Online:2014-02-25 Published:2013-07-15
Supported by:
National Natural Science Foundation of China (51105374)

摘要/Abstract

摘要：

针对单一传感器的测量信息难以准确、全面地反映航空发动机转子、轴承和齿轮的工作状况，进而造成振动故障诊断难度大的问题，提出安装多个振动传感器组成传感器网络，建立基于多传感器信息的发动机转子故障决策融合诊断系统。由于多传感器系统不可避免地会存在各传感器信息不一致、信息冲突的情形，因此针对该融合诊断系统的信号测量、信息预处理、特征提取、故障诊断及决策融合5个环节，重点研究了决策融合环节的Dempster-Shafer（D-S）证据决策融合方法存在的冲突证据融合失效问题。通过分析原因，从避免“一票否决”现象和证据加权平均两个方面进行改进，提出了改进D-S证据融合方法，并应用于航空发动机转子的模拟故障决策融合诊断中。结果表明基于D-S证据理论对3个传感器的单一诊断结果进行决策融合，能得到比任一单个传感器更准确、可靠的结果；而改进D-S证据融合方法由于能在一定程度上克服冲突证据融合带来的失效问题，且能同时兼顾处理好非冲突证据的融合，故其对于证据冲突和非冲突情形都取得了较好的融合效果，因此总的分类正确率要高于常规D-S算法和PCR5算法。

关键词: 航空发动机, 转子, 故障诊断, 决策融合, Dempster-Shafer理论, 多传感器信息

Abstract:

As the information measured by a single sensor can not reflect the working status of aero-engine rotors, bearings and gears accurately and completely, it is difficult to make vibration fault diagnosis based on it. In an attempt to solve this problem, several sensors are used to establish a sensor network. Thus, an aero-engine rotor decision fusion diagnosis based on multi-sensor information is proposed in this paper. However, information inconformity and conflict of different sensors is inevitable in a multi-sensor system, which is composed of five segments: signal measurement, signal pretreatment, feature extraction, fault diagnosis and decision fusion. A focused study is conducted on conflict evidence fusion failing of the Dempster-Shafer (D-S) evidence decision fusion method in the decision fusion segment. Through a cause analysis, improvement is proposed to avoid the "one ticket veto" phenomenon and ameliorate the weighted average evidence process. Based on the improvement, an improved D-S evidence fusion method is put forward, which is applied to the decision fusion diagnosis of a simulated aero-engine rotor vibration fault. The result shows that, when the diagnosis result of all sensors is conducted to realize decision fusion using the D-S evidence theory, the fusion result is more accurate and reliable than any single sensor result. The improved D-S evidence fusion method can overcome the failing brought by conflict evidence fusion. Consequently, a better fusion result can be achieved in both the evidence conflict and non-conflict situation. And holistic classification accuracy is higher than general D-S algorithm and PCR5 algorithm.

Key words: aero-engine, rotor, fault diagnosis, decision fusion, Dempster-Shafer theory, multi-sensor information

中图分类号:

V263.6

胡金海, 余治国, 翟旭升, 彭靖波, 任立通. 基于改进D-S证据理论的航空发动机转子故障决策融合诊断研究[J]. 航空学报, 2014, 35(2): 436-443.

HU Jinhai, YU Zhiguo, ZHAI Xusheng, PENG Jingbo, REN Litong. Research of Decision Fusion Diagnosis of Aero-engine Rotor Fault Based on Improved D-S Theory[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2014, 35(2): 436-443.

参考文献

[1] General Editorial Council of Aeroengine Design Manual. Aeroengine design manual. Rotor dynamics and whole body vibration(19)[M]. Beijing: Aviation Industry Press, 2000. (in Chinese) 航空发动机设计手册总编委会. 航空发动机设计手册. 第19册, 转子动力学及整机振动[M]. 北京: 航空工业出版社, 2000.

[2] Volponi A. Data fusion for enhanced aircraft engine prognostics and health management, NASA CR-214055[R]. East Hartford: NASA, 2005.

[3] Yang J P, Huang H Z, Miao Q. Diagnosis method of aeroengine early fault based on the Dempster-Shafer evidence theory[J]. Journal of Aerospace Power, 2008, 23(12): 2327-2331. (in Chinese) 杨建平, 黄洪钟, 苗强. 基于证据理论的航空发动机早期故障诊断方法[J]. 航空动力学报, 2008, 23(12): 2327-2331.

[4] Lu F, Huang J Q, Chen Y. Research on performance fault fusion diagnosis of aero-engine component[J]. Journal of Aerospace Power, 2009, 24(7): 1649-1653. (in Chinese) 鲁峰, 黄金泉, 陈煜. 航空发动机部件性能故障融合诊断方法研究[J]. 航空动力学报, 2009, 24(7): 1649-1653.

[5] Chen L B, Song L Q, Chen G, et al. Study on fusion diagnosis techniques of wear faults in synthesized monitoring of aero-engine[J]. Journal of Aerospace Power, 2009, 24(1): 169-175. (in Chinese) 陈立波, 宋兰琪, 陈果, 等. 航空发动机滑油综合监控中的磨损故障融合诊断研究[J]. 航空动力学报, 2009, 24(1): 169-175.

[6] Yager R. On the Dempster-Shafer framework and new combination rules[J]. Information Sciences, 1987, 1(2): 93-138.

[7] Chen T, Sun J G, Hao Y. Neural network and Dempster-Shafter theory based fault diagnosis for aeroengine gas path[J]. Acta Aeronautica et Astronautica Sinica, 2006, 27(6): 1014-1017. (in Chinese) 陈恬, 孙健国, 郝英. 基于神经网络和证据融合理论的航空发动机气路故障诊[J]. 航空学报, 2006, 27(6): 1014-1017.

[8] Deng Y, Shi W K, Zhu Z F. Efficient combination approach of conflict evidence[J]. Journal Infrared Millimeterand Waves, 2005, 23(1): 27-32. (in Chinese) 邓勇, 施文康, 朱振福. 一种有效处理冲突证据的组合方法[J]. 红外与毫米波学报, 2005, 23(1): 27-32.

[9] Hou J, Miao Z, Pan Q. A adaptive integration algorithms with DST and DSmT[J]. Microelectronics & Computer, 2006, 23(10): 150-152. (in Chinese) 侯俊, 苗壮, 潘泉. DST与DSmT自适应融合算法[J]. 微电子学与计算机, 2006, 23(10): 150-152.

[10] Sun Y, Bentabet L. A particle filtering and DSmT based approach for conflict resolving in case of target tracking with multiple cues[J]. Journal of Mathematical Imaging and Vision, 2010, 36(2): 159-167.

[11] Smarandache F, Dezert J. Proportional conflict redistribution rules for information fusion[M][C]//Smarandache F, Dezert J. Applications and advances of DSmT for information fusion. Rehoboth: American Research Press, 2006:3-68.

[12] Zhai X S, Hu J H, Xie S S, et al. Diagnosis of aero-engine with early vibration fault symptom using DSmT[J]. Journal of Aerospace Power, 2012, 27(2): 301-306. (in Chinese) 翟旭升, 胡金海, 谢寿生, 等. 基于DSmT的航空发动机早期振动故障融合诊断方法[J].航空动力学报, 2012, 27(2): 301-306.

[13] Hu J H, Xie S S, Luo G Q, et al. Fusion diagnosis of aero-engine wearing condition based on Dempster-Shafer proof theory[J]. Mechanical Science and Technology for Aerospace Engineering, 2008, 27(3): 343-346. (in Chinese) 胡金海, 谢寿生, 骆广琦, 等. 基于Dempster-Shafer证据理论的航空发动机磨损状况融合诊断[J]. 机械科学与技术, 2008, 27(3): 343-346.

[14] Ding Y Y, Li H R. A simple and effective improved D-S method for conflict evidence[J]. Command Control & Simulation, 2011, 33(2): 22-25. (in Chinese) 丁迎迎, 李洪瑞. 一种简单有效的处理冲突证据的D-S改进方法[J]. 指挥控制与仿真, 2011, 33(2): 22-25.

[15] Liang W, Wei H F, Zhou F. Fusion method of conflict evidence in D-S theory[J]. Computer Engineering and Applications, 2011, 47(6): 144-147.(in Chinese) 梁威, 魏宏飞, 周锋. D-S证据理论中一种冲突证据的融合方法[J].计算机工程与应用, 2011, 47(6): 144-147.

[16] Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London Series A: Mathematical Physical and Engineering Science, 1998, 454(1971): 903-995.

[17] Yang J M, Tian Y. Roller bearing fault diagnosis by using empirical mode decomposition and sphere-structured support vector machine[J]. Journal of Vibration, Measurement & Diagnosis, 2009, 29(2): 155-158. (in Chinese) 杨洁明, 田英. 基于EMD和球结构SVM的滚动轴承故障诊断[J]. 振动、测试与诊断, 2009, 29(2): 155-158.

E-mail：hkxb@buaa.edu.cn

关于我们

期刊社服务

专业学科

封面文章

友情链接

主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

基于改进D-S证据理论的航空发动机转子故障决策融合诊断研究

Research of Decision Fusion Diagnosis of Aero-engine Rotor Fault Based on Improved D-S Theory

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	尹泽勇, 李概奇, 石建成, 银越千. 先进通用核心机派生发展的理念、方法及实践[J]. 航空学报, 2024, 45(7): 29713-029713.
[2]	黄维娜, 黎方娟, 祁宏斌. 航空发动机数字工程初步研究与发展思考[J]. 航空学报, 2024, 45(5): 529693-529693.
[3]	齐国宁, 吴宝海, 符江锋. 高速高压燃油齿轮泵典型卸荷槽对比分析[J]. 航空学报, 2024, 45(5): 529666-529666.
[4]	马瑞贤, 王鑫, 王开明, 李斌, 廖明夫, 王四季. 航空发动机篦齿⁃橡胶涂层机匣碰摩实验[J]. 航空学报, 2024, 45(4): 628350-628350.
[5]	吴志渊, 赵林川, 颜格, 胡海峰, 杨志勃, 张文明. 转轴-轮盘-裂纹叶片耦合系统的叶尖振动特性[J]. 航空学报, 2024, 45(4): 628346-628346.
[6]	陈立芳, 孙亚冰, 周书华, 高强, 乔保栋, 李栋. 基于真实数据反演的风扇转子本机平衡方法[J]. 航空学报, 2024, 45(4): 628321-628321.
[7]	国玉林, 杨丰宇, 姚剑飞, 焦世文, 张泽良. 基于多重优化的多级盘转子虚拟装配平衡方法[J]. 航空学报, 2024, 45(4): 628323-628323.
[8]	胡明辉, 高金吉, 江志农, 王维民, 邹利民, 周涛, 凡云峰, 王越, 冯家欣, 李晨阳. 航空发动机振动监测与故障诊断技术研究进展[J]. 航空学报, 2024, 45(4): 630194-630194.
[9]	肖袁, 冯坤, 胡明辉, 江志农. 航空发动机转子非稳态振动分量提取方法[J]. 航空学报, 2024, 45(3): 228158-228158.
[10]	黄湛钧, 董鑫, 卢沐宇, 张瑞涛, 闫钊阳, 张安. 基于DRSN与电压幅值分析的航空HVDC系统逆变器故障诊断[J]. 航空学报, 2024, 45(3): 328685-328685.
[11]	李帅, 李启行, 谌璨, 方志法, 王维民. 融合柔性基础传递函数的转子系统建模方法及验证[J]. 航空学报, 2024, 45(3): 229250-229250.
[12]	张超, 曹勇, 赵振强, 张海洋, 孙建波, 王志华, 蔚夺魁. 树脂基复合材料在民用航空发动机中的应用与关键技术研究进展[J]. 航空学报, 2024, 45(2): 28556-028556.
[13]	司和勇, 王瑶俐, 曹丽华, 陈东超. 超临界二氧化碳涡轮升速过渡中密封-转子系统的动力学行为[J]. 航空学报, 2024, 45(2): 228652-228652.
[14]	李天晴, 王维民, 张旭龙, 王树慧, 付振宇. 基于叶尖定时的转子叶片轴向位移辨识方法[J]. 航空学报, 2024, 45(2): 228682-228682.
[15]	李洁, 黄文新, 蔡一鸣, 王思远, 高宇飞, 蒋雪峰. 基于DFPMM的位置传感器故障诊断与容错控制[J]. 航空学报, 2024, 45(10): 329307-329307.