@inproceedings{95493e1ba3714e058a8f7789f8ebc945,
title = "An Enhanced Network Learning Method for Dynamic Probabilistic LCF Evaluation of Turbine Blisk",
abstract = "To reasonably predict the blisk LCF life, an enhanced network learning method (ENLM) was proposed by integrating neural network of regression with extremum thought. The developed ENLM was mathematically modeled and the method of reliability analysis with the ENLM were investigated. Then the LCF life reliability of a turbine blisk were evaluated utilizing the proposed ENLM by considering thermal-structural interaction and the stochastic characteristics of parameters. It is illustrated in the analysis to the reliability degree of blisk (0.998, 5) and blisk LCF life (9, 419 cycles) under the allowable value 6, 000 cycles, which ensures some life margin for the blisk relative to the deterministic analysis. The comparison of methods reveals that the proposed ENLM has highly-computing efficiency and precision. The advantages of this method will more outstand with the increasing simulations. The outcome of this paper is to develop a promising tool for the reliability-based design optimization of gas turbine blisk LCF life in future work.",
keywords = "ENLM, extremum response, LCF life, probabilistic analysis, turbine blisk",
author = "Fei, {Cheng Wei} and Cheng Lu and Tang, {Wen Zhong}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 10th Prognostics and System Health Management Conference, PHM-Qingdao 2019 ; Conference date: 25-10-2019 Through 27-10-2019",
year = "2019",
month = oct,
doi = "10.1109/PHM-Qingdao46334.2019.8942845",
language = "英语",
series = "2019 Prognostics and System Health Management Conference, PHM-Qingdao 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Wei Guo and Steven Li and Qiang Miao",
booktitle = "2019 Prognostics and System Health Management Conference, PHAI-Qingdao 2019",
}