摘要
针对航空部附件状态退化预测问题,提出了一种经验模态分解(EMD)和自组织特征映射(SOFMs)量化误差模型相结合的部附件退化趋势预测方法。采集部附件多个特征指标的状态监测信息,运用EMD提取包含微弱信号的特征信息,并消除部分噪声干扰;运用SOFMs实现多源传感器信息融合,并建立最小量化误差(MQE)模型,量化部附件运行状态,以实现部附件的状态退化预测。通过对某型航空陀螺仪的仿真验证表明,EMD-SOFMs量化误差模型能够有效、准确地提取陀螺仪状态信息,融合量化陀螺仪运行状态,实现陀螺仪的状态退化预测。
For predicting the status degradation of aviation accessories, the method combining empirical mode decomposition ( EMD) and selforganizingfeature maps ( SOFMs) quantization error model is put forward. The status monitoring information about multiple feature indexes ofaccessories is collected; by using EMD,the feature information containing weak signals is extracted,and partial noise interference is eliminated.Multi - source sensor information fusion is realized through SOFM, and the minimum quantization error ( MQE) model is established; to quantifythe running status of accessories and implement prediction of status degradation of accessories. A certain type of aviation gyroscope is selectedfor conducting simulation and verification. Examples of analysis and verification show that EMD - SOFMs quantization error model doeseffectively and precisely extract the status information of gyroscope, fuse and quantify operating status of gyroscope, and implement the predictionof status degradation for gyroscope.
出处
《自动化仪表》
CAS
2016年第8期25-29,共5页
Process Automation Instrumentation
关键词
经验模态分解
自组织特征映射
误差模型
航空陀螺仪
多源传感器
预测
特征提取
信息融合
Empirical mode decomposition Self - organizing feature maps Error model Aviation gyroscope Multi - source senserPrediction Feature extraction Information fusion