Anomaly detection by prediction for health monitoring of satellites using LSTM neural networks

Anomaly detection in satellite has not been well-documented due to the unavailability of satellite data, while it becomes more and more important with the increasing popularity of satellite applications. Our work focus on the anomaly detection by prediction on the dataset from the satellite, where we try and compare performance among recurrent neural network (RNN), Long Short-Term Memory (LSTM) and conventional neural network (NN). We conclude that LSTM with input length p=16, dimensionality n=32, output length q=2, 128 neurons and without maximum overlap is the best in terms of balanced accuracy. And LSTM with p=128, n=32, q=16, 128 and without maximum overlap outperforms most with respect to AUC metric. We also invent award function as a new performance metric trying to capture not only the correctness of decisions that NN made but also the amount of confidence in making its decisions, and we propose two candidates of award function. Regrettably, they partially meet our expectation as they possess a fatal defect which has been proved both from practical and theoretical viewpoints.