基于数据融合的航空发动机排气温度测量
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摘要
针对某型飞机发动机排气温度测量采用传感器配置不合理、算法存在局限性,不能对故障传感器进行有效识别等问题,对传感器重新布局设计,通过小波变换对数据进行预处理,去除其中的噪声因素,利用多传感器数据融合技术进行深入优化,并检验了在传感器故障情况下各算法的辨识性。最后通过实验对5种算法进行对比分析,结果表明基于数据融合的处理方法具有更高的精确度和故障识别能力。
Aiming at problems such as irrational configuration of aeroengine exhaust temperature sensor, limitation of the existing algorithms, fail of fault sensor recognition for a certain type of aircraft, sensors are re-distributed, by using wavelet transform for data prepossessing and removing noise factors among them. Multi-sensor data fusion technology is used for a deeper optimization, and the algorithm identifiability is tested under condition of sensor fault. Finally, five kinds of algorithms are compared and analyzed through experiment. Results show that algorithms based on data fusion can obtain higher accuracy and better fault identification capability.
Aiming at problems such as irrational configuration of aeroengine exhaust temperature sensor, limitation of the existing algorithms, fail of fault sensor recognition for a certain type of aircraft, sensors are re-distributed, by using wavelet transform for data prepossessing and removing noise factors among them. Multi-sensor data fusion technology is used for a deeper optimization, and the algorithm identifiability is tested under condition of sensor fault. Finally, five kinds of algorithms are compared and analyzed through experiment. Results show that algorithms based on data fusion can obtain higher accuracy and better fault identification capability.
引文
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[2]吴虹,季江华.发动机排气温度测量方法分析[J].测控技术,2014,33(S1):146-151.
[3]MEHRANBOD N,SOROUSH M,PANJAPORNPON C.A method of sensor fault detection and identification[J].Journal of Process Control,2005,15(3):321-339.
[4]王祁.传感器信息处理及应用[M].北京:科学出版社,2012.
[5]薛生虎,李东升,叶子弘,等.基于多传感器融合技术的PCR仪温度校准系统[J].仪器仪表学报,2011,32(6):1232-1237.
[6]刘晖,陈小惠,刘银峰.基于粗糙集和模糊Kohonen聚类网络的多传感器数据融合[J].电子测量与仪器学报,2010,24(3):218-223.
[7]BARANIUK R G,CEVHER V,DUARTE M F,et al.Model based compressive sensing[J].Information Theory,2010,56(4):1982-2001.
[8]岳元龙,左信,罗雄麟.提高测量可靠性的多传感器数据融合有偏估计方法[J].自动化学报,2014,40(9):1843-1852.
[9]吕艳新,顾晓辉.多传声器小波多尺度信息融合滤波算法[J].仪器仪表学报,2012,33(4):788-794.
[10]蔡世清,周杰.基于支持向量机的多传感器数据融合算法[J].计算机工程与设计,2016,37(5):1352-1356.
[11]孙勇,景博.基于支持度的多传感器一致性融合[J].传感技术学报,2005,18(3):537-539.
[12]张建业,张鹏.飞行数据的时间序列分析方法及其应用[M].北京:国防工业出版社,2013.
[13]许桢英,罗来齐,王匀,等.基于PCA的管道缺陷导波信号特征优化方法[J].电子测量技术,2016,39(4):160-163.
[14]费文.多传感器自适应加权数据融合在测量排气温度的应用[J].国外电子测量技术,2007,26(12):4-6.
[15]林海军,滕召胜,刘让周,等.基于自适应加权融合的汽车衡故障传感器预估[J].仪器仪表学报,2010,31(7):1509-1513.