Anomaly detection for satellite power subsystem with associated rules based on Kernel Principal Component Analysis

作者：Pan, Dawei; Liu, Datong^*; Zhou, Jun; Zhang, Guoyong

来源：Microelectronics Reliability, 2015, 55(9-10): 2082-2086.

DOI：10.1016/j.microrel.2015.07.010

• Anomaly detection
• Associated rules
• KPCA
• Satellite power subsystem

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.

• 出版日期2015-9
• 单位上海卫星工程研究所; 哈尔滨工程大学; 哈尔滨工业大学