Unsupervised anomaly detection of polluted telemetry data based on a reconstruction model with adaptive weighting

Anomaly detection of telemetry data is a promising way for ensuring the reliability of spacecraft and success of space mission. This study proposes a reconstruction model with adaptive weighting for unsupervised anomaly detection of polluted telemetry data. The reconstruction model integrates the advantages of typical reconstruction models and can generate high-quality results. An adaptive weighting module is designed to against the data pollution issue by assigning different weights to training data samples. Experiments are conducted on two public telemetry datasets, and the results verify the effectiveness of our method.