Anomaly detection of satellite telemetry based on optimized extreme learning machine

Abstract In aerospace, anomaly detection based on telemetry data is a critical satellite health monitoring task that is important for identifying unusual or unexpected events and for taking measurements to improve system safety and avoid serious problems. This paper introduces a novel optimized predictive model for detecting anomalies using the Grey Wolf Optimization (GWO) algorithm and an Extreme Learning Machine (ELM), called GWO-ELM. The proposed GWO-ELM is used to find anomalous events by comparing the actual observed values with the predicted intervals of telemetry data; the GWO is applied to optimize the ELM’s input weights and the bias parameters of hidden neurons to improve its prediction accuracy and ability to detect anomalies. A performance evaluation of GWO-ELM is conducted on the NASA shuttle valve benchmark dataset, which contains samples of Labeled anomalies and various metrics are collected. The experimental results for GWO-ELM show that it makes predictions with high efficiency, is stable when detecting anomalies, and requires little computational time. In addition, the results of GWO-ELM compared with those of the basic ELM algorithm with randomized parameter selection and a support vector machine (SVM), demonstrate the effectiveness and superiority of the proposed model.