A model for equipment deterioration prediction in aviation industry using deep learning methods (case study: airplane turbofan engine)

One of the main propositions of predictive maintenance is Prognostics and Health Management (PHM) which plays a unique role in identifying, diagnosing, and predicting the health status of physical assets. To that end, one of the fundamental solutions is to assess the condition of the equipment in the aviation sector in order to provide maintenance plans by determining the trend of deterioration or destruction. In this study, a developed model of an artificial neural network was presented, focusing on the concept of deep learning and its comparison with other conventional methods, in response to the limitations and uncertainties in traditional prediction methods in determining the deterioration process of the equipment. The comparative results revealed that the deep learning neural network method with a prediction accuracy of 94% had a high performance in determining the deterioration process in aircraft turbine engines compared to other conventional methods. The findings of this study can be used to predict the remaining useful life of aviation industry equipment and to provide appropriate maintenance programs.