基于小波包PCA的故障传感器数据重构方法

Data reconstruction method for faulty sensor based on wavelet package and PCA

讨论了基于小波包的多尺度主元分析方法应用于故障传感器数据重构问题。传统的基于小波包的多尺度主元分析在进行传感器故障诊断时没有建立数据重构模型,在相关传感器信号进行小波包分解的基础上,在最佳数的所有节点上建立主元分析模型,将主元分析模型的重构结果组合后再进行小波逆变换,从而实现故障传感器的数据重构。最后,利用试车台液氢供应系统的传感器数据仿真了几种典型传感器故障,并对设计模型实现数据重构的实用性和有效性进行了验证。

Multi-scale principal component analysis based on wavelet package for data reconstruction of the faulty sensor is discussed. Conventional MSPCA based on wavelet package can not establish the model for data reconstruction of the faulty sensor. So, the PCA modals are established at each node of the best tree after the wavelet package decomposition. When the PCA models reconstruct the faulty sensor, reverse wavelet transformation is implemented to achieve the final reconstruction result. Finally, several sensor fault modes are simulated with the sensor data of the ground testing bed hydrogen providing system. And the applicability and effectiveness of the proposed modal is illustrated by these modes.