基于数据融合的航空发动机排气温度测量

Research on aeroengine exhaust temperature measurement based on data fusion

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 theexisting algorithms, fail of fault sensor recognition for a certain type of aircraft, sensors are re-distributed, byusing wavelet transform for data prepossessing and removing noise factors among them. Multi-sensor data fusiontechnology is used for a deeper optimization, and the algorithm identifiability is tested under condition of sensorfault. Finally, five kinds of algorithms are compared and analyzed through experiment. Results show thatalgorithms based on data fusion can obtain higher accuracy and better fault identification capability.