A Reliability Accelerated Degradation Model for Turbine Wheel of Super charger Based on Neural Network Nonlinear Fitting Method

Chenhang Dai; Lei Yang; Yinning Wang; Xinyuan Liu; Yajuan Liu; Xiaojian Yi

doi:10.1109/SDPC52933.2021.9563436

A Reliability Accelerated Degradation Model for Turbine Wheel of Super charger Based on Neural Network Nonlinear Fitting Method

Chenhang Dai, Lei Yang, Yinning Wang, Xinyuan Liu, Yajuan Liu, Xiaojian Yi^*

^*Corresponding author for this work

School of Mechatronical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

This paper establishes a supercharger turbo wheel reliability accelerated degradation model which is based on neural network nonlinear fitting. First of all, by the analysis of 120 hours supercharger structure test and 350 hours engine whole machine reliability test, the limit of durability of the material is determined as degradation degree and temperature and rotational speed are determined as sensitive stresses. Then, according to the method of neural network nonlinear fitting, the K418 material durable performance data are supplemented. Furthermore, from the above information, we propose two turbine wheel reliability accelerated degradation models, which include 120 hours supercharger structure test model and 350 hours engine whole machine reliability test model. Finally, the method of turbo wheel residual lasting life assessment is proposed with the help of the proposed models, and a conclusion that the 120 hours structural assessment test of the supercharger meets the 350 hours whole machine reliability assessment test is obtained.

Original language	English
Title of host publication	Proceedings of 2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2021
Editors	Xuyun Fu, Shengcai Deng, Diego Cabrera, Yongjian Zhang, Zhiqiang Pu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	270-274
Number of pages	5
ISBN (Electronic)	9781665449762
DOIs	https://doi.org/10.1109/SDPC52933.2021.9563436
Publication status	Published - 2021
Event	2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2021 - Weihai, China Duration: 13 Aug 2021 → 15 Aug 2021

Publication series

Name	Proceedings of 2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2021

Conference

Conference	2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2021
Country/Territory	China
City	Weihai
Period	13/08/21 → 15/08/21

Keywords

Life evaluation
Reliability test
turbine wheel

Access to Document

10.1109/SDPC52933.2021.9563436

Cite this

Dai, C., Yang, L., Wang, Y., Liu, X., Liu, Y., & Yi, X. (2021). A Reliability Accelerated Degradation Model for Turbine Wheel of Super charger Based on Neural Network Nonlinear Fitting Method. In X. Fu, S. Deng, D. Cabrera, Y. Zhang, & Z. Pu (Eds.), Proceedings of 2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2021 (pp. 270-274). (Proceedings of 2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SDPC52933.2021.9563436

Dai, Chenhang ; Yang, Lei ; Wang, Yinning et al. / A Reliability Accelerated Degradation Model for Turbine Wheel of Super charger Based on Neural Network Nonlinear Fitting Method. Proceedings of 2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2021. editor / Xuyun Fu ; Shengcai Deng ; Diego Cabrera ; Yongjian Zhang ; Zhiqiang Pu. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 270-274 (Proceedings of 2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2021).

@inproceedings{fa8e2d11d99c4cc38781b1a112465966,

title = "A Reliability Accelerated Degradation Model for Turbine Wheel of Super charger Based on Neural Network Nonlinear Fitting Method",

abstract = "This paper establishes a supercharger turbo wheel reliability accelerated degradation model which is based on neural network nonlinear fitting. First of all, by the analysis of 120 hours supercharger structure test and 350 hours engine whole machine reliability test, the limit of durability of the material is determined as degradation degree and temperature and rotational speed are determined as sensitive stresses. Then, according to the method of neural network nonlinear fitting, the K418 material durable performance data are supplemented. Furthermore, from the above information, we propose two turbine wheel reliability accelerated degradation models, which include 120 hours supercharger structure test model and 350 hours engine whole machine reliability test model. Finally, the method of turbo wheel residual lasting life assessment is proposed with the help of the proposed models, and a conclusion that the 120 hours structural assessment test of the supercharger meets the 350 hours whole machine reliability assessment test is obtained.",

keywords = "Life evaluation, Reliability test, turbine wheel",

author = "Chenhang Dai and Lei Yang and Yinning Wang and Xinyuan Liu and Yajuan Liu and Xiaojian Yi",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2021 ; Conference date: 13-08-2021 Through 15-08-2021",

year = "2021",

doi = "10.1109/SDPC52933.2021.9563436",

language = "English",

series = "Proceedings of 2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "270--274",

editor = "Xuyun Fu and Shengcai Deng and Diego Cabrera and Yongjian Zhang and Zhiqiang Pu",

booktitle = "Proceedings of 2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2021",

address = "United States",

}

Dai, C, Yang, L, Wang, Y, Liu, X, Liu, Y & Yi, X 2021, A Reliability Accelerated Degradation Model for Turbine Wheel of Super charger Based on Neural Network Nonlinear Fitting Method. in X Fu, S Deng, D Cabrera, Y Zhang & Z Pu (eds), Proceedings of 2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2021. Proceedings of 2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2021, Institute of Electrical and Electronics Engineers Inc., pp. 270-274, 2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2021, Weihai, China, 13/08/21. https://doi.org/10.1109/SDPC52933.2021.9563436

A Reliability Accelerated Degradation Model for Turbine Wheel of Super charger Based on Neural Network Nonlinear Fitting Method. / Dai, Chenhang; Yang, Lei; Wang, Yinning et al.
Proceedings of 2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2021. ed. / Xuyun Fu; Shengcai Deng; Diego Cabrera; Yongjian Zhang; Zhiqiang Pu. Institute of Electrical and Electronics Engineers Inc., 2021. p. 270-274 (Proceedings of 2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Reliability Accelerated Degradation Model for Turbine Wheel of Super charger Based on Neural Network Nonlinear Fitting Method

AU - Dai, Chenhang

AU - Yang, Lei

AU - Wang, Yinning

AU - Liu, Xinyuan

AU - Liu, Yajuan

AU - Yi, Xiaojian

PY - 2021

Y1 - 2021

N2 - This paper establishes a supercharger turbo wheel reliability accelerated degradation model which is based on neural network nonlinear fitting. First of all, by the analysis of 120 hours supercharger structure test and 350 hours engine whole machine reliability test, the limit of durability of the material is determined as degradation degree and temperature and rotational speed are determined as sensitive stresses. Then, according to the method of neural network nonlinear fitting, the K418 material durable performance data are supplemented. Furthermore, from the above information, we propose two turbine wheel reliability accelerated degradation models, which include 120 hours supercharger structure test model and 350 hours engine whole machine reliability test model. Finally, the method of turbo wheel residual lasting life assessment is proposed with the help of the proposed models, and a conclusion that the 120 hours structural assessment test of the supercharger meets the 350 hours whole machine reliability assessment test is obtained.

AB - This paper establishes a supercharger turbo wheel reliability accelerated degradation model which is based on neural network nonlinear fitting. First of all, by the analysis of 120 hours supercharger structure test and 350 hours engine whole machine reliability test, the limit of durability of the material is determined as degradation degree and temperature and rotational speed are determined as sensitive stresses. Then, according to the method of neural network nonlinear fitting, the K418 material durable performance data are supplemented. Furthermore, from the above information, we propose two turbine wheel reliability accelerated degradation models, which include 120 hours supercharger structure test model and 350 hours engine whole machine reliability test model. Finally, the method of turbo wheel residual lasting life assessment is proposed with the help of the proposed models, and a conclusion that the 120 hours structural assessment test of the supercharger meets the 350 hours whole machine reliability assessment test is obtained.

KW - Life evaluation

KW - Reliability test

KW - turbine wheel

UR - http://www.scopus.com/inward/record.url?scp=85126240057&partnerID=8YFLogxK

U2 - 10.1109/SDPC52933.2021.9563436

DO - 10.1109/SDPC52933.2021.9563436

M3 - Conference contribution

AN - SCOPUS:85126240057

T3 - Proceedings of 2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2021

SP - 270

EP - 274

BT - Proceedings of 2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2021

A2 - Fu, Xuyun

A2 - Deng, Shengcai

A2 - Cabrera, Diego

A2 - Zhang, Yongjian

A2 - Pu, Zhiqiang

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2021

Y2 - 13 August 2021 through 15 August 2021

ER -

Dai C, Yang L, Wang Y, Liu X, Liu Y, Yi X. A Reliability Accelerated Degradation Model for Turbine Wheel of Super charger Based on Neural Network Nonlinear Fitting Method. In Fu X, Deng S, Cabrera D, Zhang Y, Pu Z, editors, Proceedings of 2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 270-274. (Proceedings of 2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2021). doi: 10.1109/SDPC52933.2021.9563436

A Reliability Accelerated Degradation Model for Turbine Wheel of Super charger Based on Neural Network Nonlinear Fitting Method

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