Remaining useful life prediction for equipment based on RF-BiLSTM

Abstract

The prediction technology of remaining useful life has received a lot attention to ensure the reliability and stability of complex mechanical equipment. Due to the large-scale, non-linear, and high-dimensional characteristics of monitoring data, machine learning does not need an exact physical model and prior expert knowledge. It has robust data processing ability, which shows a broad prospect in the field of life prediction of complex mechanical and electrical equipment. Therefore, a remaining useful life prediction algorithm based on Random Forest and Bi-directional Long Short-Term Memory (RF-BiLSTM) is proposed. In the RF-BiLSTM algorithm, RF is utilized to extract health indicators that reflect the life of the equipment. On this basis, a BiLSTM neural network is used to predict the residual life of the device. The effectiveness and advanced performance of RF-BiLSTM are verified in commercial modular aviation propulsion system datasets. The experimental results show that the RMSE of the RF-BiLSTM is 0.3892, which is 47.96%, 84.81%, 38.89%, and 86.53% lower than that of LSTM, SVR, XGBoost, and AdaBoost, respectively. It is verified that RF-BiLSTM can effectively improve the prediction accuracy of the remaining useful life of complex mechanical and electrical equipment, and it has certain application value.