In various fields, such as medical engi-neering or aerospace engineering, it is difficult to apply the decisions of a machine learning (ML) or a deep learning (DL) model that do not account for the vast amount of human limitations which can lead to errors and incidents. Explainable Artificial Intelligence (XAI) comes to explain the results of artificial intelligence software (ML or DL) still considered black boxes to understand their decisions and adopt them. In this paper, we are interested in the deployment of a deep neural network (DNN) model able to predict the Remaining Useful Life (RUL) of a turbofan engine of an aircraft. Shapley s method was then applied in the explanation of the DL results. This made it possible to determine the participation rate of each parameter in the RUL and to identify the most decisive parameters for extending or shortening the RUL of the turbofan engine.