تشخیص ناهنجاری برای زیر سیستم قدرت ماهواره ای با قوانین مرتبط مبتنی بر آنالیز اجزای اساسی هسته

The paper presents an implementable method of anomaly detection for satellite power system. Specifically, a data-driven anomaly detection method for sensor data integrated Kernel Principal Component Analysis (KPCA) and association rule mining is demonstrated. Establishing associated rules among sensor monitoring data sets, this approach analyses the structure of measure space via its Eigen matrix with KPCA, and identifies the anomaly. Especially, different anomalies from satellite system and sensors can be distinguished with the changes of association rules. The effectiveness of the method is proved on sensor data from Feng-Yun satellite power subsystem.