Abstract: Aiming at the problem of sensor fault diagnosis and fault data reconstruction, a hybrid algorithm model based on improved Long Short-Term Memory (LSTM) and Random Forest (RF) is proposed. Firstly, the improved LSTM is used to predict the output sequence of the sensor, and the residual sequence is obtained by the difference between the predicted value and the actual value. Secondly, the residual sequence is classified by the RF algorithm to identify the fault state of the sensor. When the sensor is in fault state after diagnosis, the fault data is reconstructed by using the prediction value of the improved LSTM. The improved LSTM-RF algorithm cannot only diagnose the sensor fault, but also reconstruct the fault data. The experimental results show that the accuracy of the proposed algorithm  is more than 97% on different data sets, and the RMSE of fault data reconstruction is less than 4%. Compared with the standard LSTM-RF, the improved LSTM-RF algorithm improves the convergence speed and the accuracy of fault data reconstruction by 0.4%.

Key words: sensor, fault diagnosis, fault data reconstruction, improved long short-term memory, random forest