基于深度学习和随机森林的飞行器传感器故障诊断OA

The high reliability of sensors has a crucial impact on the control and navigation positioning of aircraft.Sudden sensor failures during the execution of aircraft missions can lead to the aircraft control system and navigation system receiving incorrect information,which in turn causes system instability and mission failure.In response to the low operational efficiency of traditional long short-term memory(LSTM)network algorithms in aircraft sensor fault diagnosis,this paper proposes a hybrid algorithm based on an improved LSTM and random forest(RF)to achieve efficient diagnosis of aircraft sensor faults.Firstly,the sensor fault diagnosis problem is transformed into a classification problem.The sensor data is predicted using a long short-term memory network model with a fused attention mechanism,and the working state of the sensor is reflected through the residual sequence generation method.Secondly,the residual sequence is subjected to feature extraction and classification using the random forest model to determine whether the sensor has failed and the corresponding fault type.Finally,the effectiveness of the method is verified through simulation experiments.This method can effectively improve the accuracy and robustness of sensor fault diagnosis,providing reliable technical support for the safe operation of aircraft.