Long-Term Prediction of Remaining Useful Life for Industrial IoT

Industrial Internet of Things (IIoT), a branch of the Internet of Things (IoT) for the industrial sector, plays a vital role in integrating industrial equipment, monitoring equipment health, and improving the overall efficiency of industrial production process. Accurately predicting the remaining useful life (RUL) of IIoT equipment is a crucial task in prognostic health management (PHM), which analyzes the degradation trend of industrial equipment to schedule maintenance activi-ties in a timely manner. Artificial Intelligence (AI) techniques, such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory Networks (LSTMs), have been widely used in RUL prediction. However, these techniques face challenges in incorporating long-sequence information to capture degradation trends and predicting long-term RUL values. In this paper, we propose an Informer-based method, Co-Informer, for long-term RUL prediction. Co-Informer utilizes a series of sensor data to provide the predicted RUL values during an upcoming time window. In our research, extensive experiments are carried out with C-MAPSS, a widely used turbofan engine degradation dataset provided by NASA. Our experimental results indicate that Co-Informer outperforms the state-of-the-art schemes for RUL prediction in terms of Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE).