A Data Mining Model for Anomaly Detection of Satellite Launch Vehicle

Anomaly detection refers to the problem of finding patterns in data that do not conform to expected behavior. Development of advanced anomaly detection and failure diagnosis technologies for satellite launch vehicle (SLV) is a quite significant issue in the aerospace industry, because the space environment is harsh, distant and uncertain.Current SLV health monitoring and fault diagnosis practices involve around-the-clock limit-checking or simple trend analysis using text or graphical displays on large amount of telemetry data. This procedure, which requires large numbers of human experts, is of course cumbersome and time-consuming. Furthermore, humans are not always able to recognize anomalous situations. In this paper, a systematic and transparent diagnostic methodology will be proposed and developed within intelligent anomaly detectioon framework for SLV health monitoring. Experimental results show that the proposed method is capable of characterizing and monitoring interactions between multiple spacecraft parameters and can provide additional insight and valuable decision support for controllers and engineers.