Predictive Maintenance for RM12 with Machine Learning

Abstract: Few components within mechanical engineering possess the fatigue resistance as of high-pressure turbine blades found in jet engines. This as they are designed to perform in extensively high temperatures under severe loading which causes degradation to be an important aspect despite a design, optimized for its environment. This study aims to find a method for predicting life consumption of those blades belonging to the turbine section of the jet engine in JAS 39 Gripen C/D called RM12. This was performed at GKN Aerospace, which holds the military type certificate for this engine as well as a patented solution that determines life consumption in components depending on operational history. With the help of machine learning in Matlab, flight sensor data and loading results, the method was to explore a variety of prediction models and find a selection of blades with varied utilization before reaching end of life for comparison. Followed by a search of understanding the life limiting fatigue conditions and the factors involved in the deterioration process. A similarity finding approach gave valuable meaning to the accuracy of regression analysis from flight data towards output in form of temperature predictions. Comparing known and reliable fatigue calculation results gave however no clear picture as inspected blades had reach their limit at very diverse accumulated values. The next approach was therefore to investigate if an initialization point of degradation could be found, from where the result could give an answer that matched for all blades and their different utilization. The result was that an accelerated degradation after high loading could give a prediction that could explain the total life consumption with an accuracy of 87% for 19 out of 21 investigated blades. The accelerated deterioration could in theory be explained by the fact that the fatigue resistance as well as different types of degradation, propagates each other and originates from thermal loading making them all contributors, whereas the conventional numerical methods only handles them separately. In order to get confidence, valuable and reliable predictions, the models do however need to be accompanied with more testing and adding of contributing factors before assumed as a proven method for life consumption determination of the high-pressure turbine blades.