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Title: Prognostic of RUL based on Echo State Network Optimized by Artificial Bee Colony
Authors: Amaya, Edgar J.
Álvares, Alberto José
Assunto:: Prognóstico
Redes neurais (Computação)
Algoritmos de computador
Issue Date: 2016
Publisher: International Journal of Prognostics and Health Management
Citation: AMAYA, E. ; ALVARES, A. J. Prognostic of RUL based on Echo State Network Optimized by Artificial Bee Colony (SJR 0.43). International Journal of Prognostics and Health Management, v. 7, p. 1-12, 2016. Disponível em: Acesso em: 28 fev. 2018.
Abstract: Prognostic is an engineering technique used to predict the future health state or behavior of an equipment or system. In this work, a data-driven hybrid approach for prognostic is presented. The approach based on Echo State Network (ESN) and Artificial Bee Colony (ABC) algorithm is used to predict machine’s Remaining Useful Life (RUL). ESN is a new paradigm that establishes a large space dynamic reservoir to replace the hidden layer of Recurrent Neural Network (RNN). Through the application of ESN is possible to overcome the shortcomings of complicated computing and difficulties in determining the network topology of traditional RNN. This approach describes the ABC algorithm as a tool to set the ESN with optimal parameters. Historical data collected from sensors are used to train and test the proposed hybrid approach in order to estimate the RUL. To evaluate the proposed approach, a case study was carried out using turbofan engine signals show that the proposed method can achieve a good collected from physical sensors (temperature, pressure, speed, fuel flow, etc.). The experimental results using the engine data from NASA Ames Prognostics Data Repository RUL estimation precision. The performance of this model was compared using prognostic metrics with the approaches that use the same dataset. Therefore, the ESNABC approach is very promising in the field of prognostics of the RUL.
Licença:: This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Appears in Collections:ENM - Artigos publicados em periódicos

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