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Title

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

Pages

  1245-1258

Abstract

 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 nally estimation of Remaining Useful Life (RUL). In the mapping step, a neural tting tool is used. To clarify the age-based clustering concept, the main elements of the ADC model is discussed. A Genetic algorithm (GA) is used to nd the elements of the optimal model. Lastly, the fuzzy technique is applied to modify the clustering. By investigating a case study on the Health monitoring of some turbofan engines, the e cacy of the proposed method is demonstrated. The results showed that the concept of clustering without optimization processes is e cient even for the simplest form of performance. However, by optimizing structure elements and fuzzy clustering, the Prognosis accuracy increased up to 71%. The e ectiveness of ADC Prognosis is proven in comparison with other methods.

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    APA: Copy

    MAHMOODIAN, A., DURALI, M., Abbasian Najafabadi, T., & SAADAT FOUMANI, M.. (2021). Optimized age dependent clustering algorithm for prognosis: A case study on gas turbines. SCIENTIA IRANICA, 28(3 (Transactions B: Mechanical Engineering)), 1245-1258. SID. https://sid.ir/paper/990365/en

    Vancouver: Copy

    MAHMOODIAN A., DURALI M., Abbasian Najafabadi T., SAADAT FOUMANI M.. Optimized age dependent clustering algorithm for prognosis: A case study on gas turbines. SCIENTIA IRANICA[Internet]. 2021;28(3 (Transactions B: Mechanical Engineering)):1245-1258. Available from: https://sid.ir/paper/990365/en

    IEEE: Copy

    A. MAHMOODIAN, M. DURALI, T. Abbasian Najafabadi, and M. SAADAT FOUMANI, “Optimized age dependent clustering algorithm for prognosis: A case study on gas turbines,” SCIENTIA IRANICA, vol. 28, no. 3 (Transactions B: Mechanical Engineering), pp. 1245–1258, 2021, [Online]. Available: https://sid.ir/paper/990365/en

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