Abstract
Spacecrafts in space environment are exposed to several kinds of thermal sources such as radiation, albedo and emitted IR from the earth. The thermal control subsystem in spacecraft is used to keep all parts operating within allowable temperature ranges. A failure in one or many temperature sensors could lead to abnormal operation. Consequently, a prediction process must be performed to replace the missing data with estimated values to prevent abnormal behavior. The goal of the proposed model is to predict the failed or missing sensor readings based on artificial neural networks (ANN). It has been applied to EgyptSat-1 satellite. A backpropagation algorithm called Levenberg-Marquardt is used to train the neural networks (NN). The proposed model has been tested by one and two hidden layers. Practical metrics such as mean square error, mean absolute error and the maximum error are used to measure the performance of the proposed network. The results showed that the proposed model predicted the values of one failed sensor with adequate accuracy. It has been employed for predicting the values of two failed sensors with an acceptable mean square and mean absolute errors; whereas the maximum error for the two failed sensors exceeded the acceptable limits.
Highlights
Spacecrafts in space environment are exposed to different thermal conditions such as Sun radiation, albedo and emitted IR from the earth (Fig. 1)
The results showed that the Mean Square Error (MSE) and Mean Absolute Error (MAE) for two hidden layers network are better than the one hidden layer network
The two hidden layers network with 30 neurons (Fig. 13) provide acceptable values for the maximum error, MSE, and MAE. This network has been tested by the third part of the dataset, and the results demonstrated that the maximum error is 6.5 °C, MSE is 0.44 and the MAE equals to 0.34
Summary
Spacecrafts in space environment are exposed to different thermal conditions such as Sun radiation, albedo (reflected sunlight from earth) and emitted IR from the earth (Fig. 1). The first solution is to discard the samples with missing values This method is known as listwise deletion or complete case analysis (Van Buuren 2018). It is not suitable for high rate missing data or temperature sensor failure in the satellite as it may result in mission failure. Since the early 2000s, a new paradigm of thinking has emerged where missing values are treated as unknown values to be learned through a machine-learning model In this framework, complete data samples are used as training set for a machine-learning model, which is applied to the data samples with missing values to impute them. The proposed networks have bee(nPntrgatirneeed.cuosminganthdefLreeevpenikb.ecrogm-M).arquardt algorithm with the EgyptSat-1 (Fig. 2) satellite temperature data to estimate the failed or missing sensor values
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