基于样本分位数的机载燃油泵故障状态特征提取及实验研究

doi:10.7527/S1000-6893.2015.0303

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基于样本分位数的机载燃油泵故障状态特征提取及实验研究

李娟^1,2, 景博¹, 羌晓清¹, 刘晓东^3,4

1. 空军工程大学航空航天工程学院, 西安 710038;
2. 鲁东大学数学与统计科学学院, 烟台 264025;
3. 中航工业金城南京机电液压工程研究中心, 南京 210000;
4. 航空机电系统综合航空科技重点实验室, 南京 210000

收稿日期:2015-08-31 修回日期:2015-11-12 出版日期:2016-09-15 发布日期:2015-11-27
通讯作者: 李娟女, 博士研究生, 讲师。主要研究方向: 故障预测与健康管理。 Tel.: 029-84787628 E-mail: daidaiquanquan123@126.com E-mail:daidaiquanquan123@126.com
作者简介:景博女, 博士, 教授, 博士生导师。主要研究方向: 故障预测与健康管理, 可测试性设计, 传感器网络, 数据融合。 E-mail: jingbo_sensor@163.com;羌小清男, 硕士研究生。主要研究方向: 故障预测与健康管理。 E-mail: 928169759@qq.com;刘晓东男, 高级工程师。主要研究方向: 飞机燃油泵及燃油系统设计。 E-mail: bettercc@sohu.com
基金资助:
航空科学基金（20142896022）

Fault states feature extraction and experimental study for airborne fuel pumps based on sample quantile

LI Juan^1,2, JING Bo¹, QIANG Xiaoqing¹, LIU Xiaodong^3,4

1. College of Aeronautics and Astronautics Engineering, Air Force Engineering University, Xi'an 710038, China;
2. College of Mathematics and Statistics, Ludong University, Yantai 264025, China;
3. Nanjing Engineering Institute of Aircraft Systems, Jincheng, AVIC, Nanjing 210000, China;
4. Aviation Science and Technology Key Laboratory of Aviation Mechanical and Electrical System, Nanjing 210000, China

Received:2015-08-31 Revised:2015-11-12 Online:2016-09-15 Published:2015-11-27
Supported by:
Aeronautical Science Foundation of China (20142896022)

摘要/Abstract

摘要：

机载燃油泵的健康状态关系着飞行任务的完成和飞行安全，对机载燃油泵的故障状态特征提取及诊断成为亟需解决的问题。通过对机载燃油实验系统的振动与压力信号进行综合分析，提出了一种基于样本分位数的故障状态特征提取方法。首先，根据样本分位数的渐近分布定理，讨论了样本分位数的统计特性，分析了故障状态与样本分位数的对应关系，从理论上保证了该方法的可行性，在实测数据统计分析的基础上，讨论了样本容量对样本分位数稳定性的影响；其次，根据样本分位数渐近分布定理计算各故障状态的置信区间，并与Bootstrap方法得到的置信区间进行对比，结果显示，依据样本分位数渐近分布定理得到的置信区间真实可靠，为在线故障诊断提供了依据；然后，以各故障状态下提取的样本分位数为特征向量构建贝叶斯判别函数，进行故障诊断；最后依据故障诊断的正确率对传感器进行优化，结果表明，同时安装振动传感器与压力传感器可以提高故障诊断的正确率，并且只安装1个压力传感器与1个特定方向的振动传感器即可对机载燃油泵的故障状态进行完全识别。为快速准确的在线判断机载燃油泵的状态提供了理论支撑，并且可以降低工程应用中机载燃油泵监测系统的体积、功耗及复杂性。

关键词: 燃油泵, 样本分位数, 传感器, 故障预测与健康管理, 故障诊断

Abstract:

The health state of airborne fuel pumps is important for the achievement and safety of flight mission, so the fault state feature extraction and diagnosis for the fuel pumps become an urgent problem. Through the analysis of the output signal about the vibration sensors and pressure sensor from the experiment system of airborne fuel pump, a sample quantile based fault state feature extraction method is presented. Firstly, by the asymptotic distribution theorem of the quantile, the statistic character of the sample quantile is discussed; the correspondence between the fault state and the sample quantile is discussed; the feasibility of the presented method is ensured in theory; statistic analysis has been done for the real data, and the stability alone with the sample size is discussed. Secondly, the confidence interval of sample quantile is computed based on the asymptotic distribution theorem, and compared with that obtained by the Bootstrap methods. The results show that the confidence interval of sample quantile obtained according to the asymptotic distribution theorem is credible, providing basis for online fault diagnosis. Then the Bayesian discriminant function is established based on the quantile feature vector extracted under different states, which will be used for the fault diagnosis. Finally, the layout of the sensors is optimized based on the accuracy of fault diagnosis. The results show that the vibration sensors combined with pressure sensor will improve the accuracy of fault diagnosis, and only one vibration sensor combined with one pressure sensor can finish the fault diagnosis completely. This study provides theoretical support for the quick and accurate online decision about the states of the fuel pump, and it can reduce volume, power consumption and complexity of the system in engineering applications.

Key words: fuel pumps, sample quantile, sensors, prognostics and health management, fault diagnosis

中图分类号:

V263.6

李娟, 景博, 羌晓清, 刘晓东. 基于样本分位数的机载燃油泵故障状态特征提取及实验研究[J]. 航空学报, 2016, 37(9): 2851-2863.

LI Juan, JING Bo, QIANG Xiaoqing, LIU Xiaodong. Fault states feature extraction and experimental study for airborne fuel pumps based on sample quantile[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2016, 37(9): 2851-2863.

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E-mail：hkxb@buaa.edu.cn

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基于样本分位数的机载燃油泵故障状态特征提取及实验研究

Fault states feature extraction and experimental study for airborne fuel pumps based on sample quantile

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