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Regularization Based Particle Swarm Optimization for Length Changeable Extreme Learning Machine under Health State Estimation of Military Aircraft Engines

Citation:

Berghout T, Mouss L-H, KADRI O. Regularization Based Particle Swarm Optimization for Length Changeable Extreme Learning Machine under Health State Estimation of Military Aircraft Engines. 8thINTERNATIONAL CONFERENCEON DEFENSESYSTEMS: ARCHITECTURES AND TECHNOLOGIES (DAT’2020) April14-16, [Internet]. 2020.

Abstract:

In this work a new data-driven approach for Remaining Useful Life estimation of aircraft engines is developed. The proposed approach is a regularized Single Hidden Layer Feedforward Neural network (SLFN) with incremental constructive enhancements. The training rules of this algorithm are inspired form different Extreme Learning Machine (ELM) variants. Particle Swarm Optimization (PSO) algorithm is integrated to enhance tracking ability of the best regularization parameter to reduce the norm of the tuned weights. The proposed approach is evaluated using C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) dataset and compared to its other derivatives and proved its accuracy. C-MAPSS software has revisions in military and civil applications. In this paper, the military version of its application is the used one.

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