Research On Communication Satellite Fault Detection Based On Recurrent Neural Network

In recent years,with the development of China ’s aerospace industry,the structure of communication satellites has become increasingly complex,accompanied by a sharp increase in the types and number of faults.At present,the industrial fault detection of satellite telemetry data is mainly based on the threshold method and expert experience method.How to realize the intelligence of fault detection has become a hot topic in the current aerospace research.Based on a large amount of telemetry data from a communication satellite provided by a space agency,this paper studies the fault detection algorithm based on recurrent neural network,proposes corresponding solutions,and compares it with various existing algorithms in the field.The experimental comparison proves the effectiveness of the proposed fault detection algorithm.The main contents and innovations of this paper are as follows:(1)This paper uses the 24-dimensional telemetry data of a communication satellite provided by the space agency for research.For each telemetry parameter,a corresponding LSTM model is trained to predict time series data.Based on the sequence prediction of LSTM,the concept of time series data deviation is designed and used to calculate the weighted Euclidean distance at each time point.Thus,the fault scores at each time point are obtained,and then a suitable threshold is selected for multi-dimensional fault detection.The experimental comparison with the existing fault detection algorithm verifies the effectiveness of the model.(2)This paper uses the trained LSTM prediction model of each telemetry parameter to predict each single parameter data.A thresholding method is proposed to select different fault judgment thresholds for each telemetry parameter,so as to perform fault detection for each single parameter.The specific parameters of the fault can be obtained at different times,and a failure determination matrix can be obtained.(3)Based on single parameter fault detection in content(2),this paper analyzes the correlation of each satellite telemetry parameter and groups the telemetry parameters.If only some parameters are alarmed at a time,but other parameters with strong correlation are not alarmed,it is considered as a non-fault point.In this way,this paper conducts system-level fault detection.Through experimental comparison with the existing fault detection algorithm,it shows the good performance of the model.(4)According to the characteristics of the above two fault detection algorithms,this paper combines the multi-dimensional fault detection and the fault detection algorithm based on single-dimensional detection and correlation analysis proposed above to perform system-level fault detection.Through experimental comparison,the model can effectively distinguish the possible misjudgments in fault detection and reduce the false alarm rate.