Optimized age dependent clustering algorithm for prognosis: A case study on gas turbines

This paper proposes an Age Dependent Clustering (ADC) structure to be used for prognostics. To achieve this aim, a step-by-step methodology is introduced, that includes clustering, reproduction, mapping and finally estimation of Remaining Useful Life (RUL). In the mapping step, neural fitting tool is used. Considering age based clustering concept, determination of main elements of the ADC model is discussed. Genetic algorithm (GA) is used to find the elements of the optimal model. Lastly, fuzzy technique is applied to modify the clustering. The efficacy of the proposed method is demonstrated with a case study on the health monitoring of some turbofan engines. The results show that the concept of clustering even without optimization processes is efficient even for the simplest form of performance. However, by optimizing structure elements and fuzzy clustering, the prognosis accuracy increases up to 71%. The effectiveness of age dependent clustering in prognosis is proven in comparison with other methods.