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
收稿日期: 2015-08-31
修回日期: 2015-11-12
网络出版日期: 2015-11-27
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
Received date: 2015-08-31
Revised date: 2015-11-12
Online published: 2015-11-27
Supported by
Aeronautical Science Foundation of China (20142896022)
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。
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 . DOI: 10.7527/S1000-6893.2015.0303
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.
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