| Campo DC | Valor | Idioma |
|---|
| dc.contributor.author | Amaya, Edgar J. | - |
| dc.contributor.author | Álvares, Alberto José | - |
| dc.date.accessioned | 2019-03-01T13:31:30Z | - |
| dc.date.available | 2019-03-01T13:31:30Z | - |
| dc.date.issued | 2016 | - |
| dc.identifier.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: http://www.phmsociety.org/sites/phmsociety.org/files/phm_submission/2015/ijphm_16_006.pdf. Acesso em: 28 fev. 2018. | pt_BR |
| dc.identifier.uri | http://repositorio.unb.br/handle/10482/34090 | - |
| dc.language.iso | Inglês | pt_BR |
| dc.publisher | International Journal of Prognostics and Health Management | pt_BR |
| dc.rights | Acesso Aberto | pt_BR |
| dc.title | Prognostic of RUL based on Echo State Network Optimized by Artificial Bee Colony | pt_BR |
| dc.type | Artigo | pt_BR |
| dc.subject.keyword | Prognóstico | - |
| dc.subject.keyword | Redes neurais (Computação) | - |
| dc.subject.keyword | Algoritmos de computador | - |
| dc.rights.license | 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. | pt_BR |
| dc.description.abstract1 | 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. | pt_BR |
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