基于循环神经网络的通信卫星故障检测

Communication Satellite Fault Detection Based on Recurrent Neural Network

随着现代航天事业的飞速发展,通信卫星的结构日益复杂,其故障也逐渐增多,通信卫星的故障检测已成为当前航天领域关注的重点问题。目前,各大航天机构对故障的检测仍以简单的上下限阈值检测为主,只能检测出少部分特定的故障。早期利用传统机器学习算法进行检测的研究也仅能检测出数量特征上的故障。针对传统的机器学习算法难以有效学习遥测数据趋势变化的问题,文中提出了基于长短时记忆(Long Short-term Memory,LSTM)网络的阈值化方法。通过LSTM预测模型来学习卫星遥测数据的趋势变化,同时以最大化相关系数与F1分数的方式为多维遥测数据的故障判定确定合适的阈值,此方式能够有效地通过卫星遥测数据的趋势变化来判断故障。实验数据采用某航天机构提供的时长为2年的24维通信卫星遥测数据,其核心模型LSTM网络在NVIDIA Corporation GP102[TITAN Xp]上训练,最终整体模型的准确率为99.34%,查准率为81.93%,查全率为94.62%。同时,与传统机器学习算法以及基于LSTM的非阈值方法进行对比,该模型的精度明显更高。实验结果表明,LSTM网络能够高效地学习到卫星遥测数据的趋势变化特征;同时,采用合适的方法选定阈值,能够有效地检测出通信卫星发生的故障,在很大程度上成功地解决航天领域中通信卫星的故障检测难题。

With the rapid development of modern spaceindustry,the structure of communication satellites is becoming more and more complex,while its faults are gradually increasing,and fault detection of communication satellites has become a key issue in the current aerospace field.At present,the detection of faults by major space agencies is still based on simple upper and lower threshold’s detection.The method is too simple and can only detect a small number of specific faults.Early studies using traditional machine learning for detection can only detect faults in quantitative characteristics.Aiming at the problem that traditional machine learning algorithms are difficult to effectively learn the trend of telemetry data,this paper proposed a thresholding method based on long-short-time memory network.LSTM prediction model is used to learn the trend change of the satellite telemetry data,and at the same time to maximize the correlation coefficient and the F1 score,to determine the appropriate threshold for the fault determination of the multi-dimensional telemetry data.This method can effectively judge the fault by the trend of the satellite telemetry data.The experimental data is based on the 24D communication satellite telemetry data provided by a space agency for 2 years.The core model LSTM network is trained on NVIDIA GTX TITAN X.The final model accuracy is 99.34%,the precision is 81.93%,and the recall rate was 94.62%.At the same time,compared with the traditional machine learning algorithm and the LSTM-based non-threshold method,the accuracy of the model is significantly higher.The experimental results show that the LSTM network can efficiently learn the trend characteristics of satellite telemetry data.At the same time,using the appropriate method to select the threshold value,it can effectively detect the faultsof the communication satellite which successfully solve the problem of communication satellite fault detection in the aerospace field.