摘要
A fault detection method based on incremental locally linear embedding(LLE)is presented to improve fault detecting accuracy for satellites with telemetry data.Since conventional LLE algorithm cannot handle incremental learning,an incremental LLE method is proposed to acquire low-dimensional feature embedded in high-dimensional space.Then,telemetry data of Satellite TX-I are analyzed.Therefore,fault detection are performed by analyzing feature information extracted from the telemetry data with the statistical indexes T2 and squared prediction error(SPE)and SPE.Simulation results verify the fault detection scheme.
A fault detection method based on incremental locally linear embedding （LLE） is presented to improve fault detecting accuracy for satellites with telemetry data. Since conventional LLE algorithm cannot handle incre mental learning, an incremental LLE method is proposed to acquire low-dimensional feature embedded in high-di mensional space. Then, telemetry data of Satellite TX-I are analyzed. Therefore, fault detection are performed by analyzing feature information extracted from the telemetry data with the statistical indexes T2 and squared prediction error（SPE） and SPE. Simulation results verify the fault detection scheme.
基金
supported by the Fundamental Research Funds for the Central Universities(No.2016083)
