@inproceedings{d87c48a790794f1eb42a2132c6868911,
title = "A modified echo state network based remaining useful life estimation approach",
abstract = "An approach to estimate the remaining useful life (RUL) by Echo State Network (ESN) is presented, which is a new paradigm in recurrent neural network (RNN). ESN randomly establishes a large sparse reservoir to replace the hidden layer of RNN, which overcomes the shortcomings of complicated computing, difficulties in determining the network topology of traditional RNN. An ESN sub-models strategy composed by classified ESN models matching to the varied training data set by retraining and classification is explored to estimate the RUL of turbofan engine system. The experimental results with the turbofan engine data of NASA Ames Prognostics Data Repository show that the proposed method can achieve better RUL estimation precision compared with the approaches of classical ESN and ESN trained by Kalman Filter and potential prospective in application.",
keywords = "Echo State Network, Kalman Filter, Prognostics and Health Management, RUL Estimation, Turbofan engine system",
author = "Yu Peng and Hong Wang and Jianmin Wang and Datong Liu and Xiyuan Peng",
year = "2012",
doi = "10.1109/ICPHM.2012.6299524",
language = "英语",
isbn = "9781467303569",
series = "PHM 2012 - 2012 IEEE Int. Conf.on Prognostics and Health Management: Enhancing Safety, Efficiency, Availability, and Effectiveness of Systems Through PHM Technology and Application, Conference Program",
booktitle = "PHM 2012 - 2012 IEEE Int. Conf. on Prognostics and Health Management:Enhancing Safety, Efficiency, Availability, and Effectiveness of Systems Through PHM Technology and Application,Conference Program",
note = "2012 IEEE International Conference on Prognostics and Health Management: Enhancing Safety, Efficiency, Availability, and Effectiveness of Systems Through PHM Technology and Application, PHM 2012 ; Conference date: 18-06-2012 Through 21-06-2012",
}