EFI System Performance Reliability Evaluation Based on Degradation Data Distribution

CUI Runlong; CHEN Tao

doi:10.1049/cje.2018.05.012

Volume 27 Issue 4

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CUI Runlong and CHEN Tao, “EFI System Performance Reliability Evaluation Based on Degradation Data Distribution,” Chinese Journal of Electronics, vol. 27, no. 4, pp. 792-798, 2018, doi: 10.1049/cje.2018.05.012

Citation:

CUI Runlong and CHEN Tao, “EFI System Performance Reliability Evaluation Based on Degradation Data Distribution,” Chinese Journal of Electronics, vol. 27, no. 4, pp. 792-798, 2018, doi: 10.1049/cje.2018.05.012

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EFI System Performance Reliability Evaluation Based on Degradation Data Distribution

doi: 10.1049/cje.2018.05.012

CUI Runlong^1,2,
CHEN Tao¹

1.
State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China;
2.
Freescale Semiconductor(China) Electronics Corporation, Tianjin 300385, China

Funds: This work is supported by the National Natural Science Foundation of China (No.51206118).

Received Date: 2016-03-15
Rev Recd Date: 2016-08-19
Publish Date: 2018-07-10

Abstract

Abstract

The performance of Electronic fuel injection (EFI) systems has been significantly improved owing to the enhancement of materials and components. Thus, it is very difficult to obtain enough time-to-failure data for a specific time. This raises a big challenge with regard to traditional reliability evaluations of EFI systems since the evaluations lack abundant failure data. To resolve the problem, this paper proposes a new method. By analyzing the working principle and failure mechanism of an EFI system, the extraction method and degradation analysis of the system parameters are first carried out. Then, a temperature stress degradation model of linear regression based on degradation data distribution is presented. Experimental results show that EFI system performance reliability can be effectively evaluated. This can provide a theoretical basis for an overall performance estimation and proactive maintenance of automotive ECUs.

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References(12)

References

Mamala Jaroslaw, Brol Sebastian and Mariusz Graba, "Engine control unit testing by hardware-in-the-loop simulation", Solid State Phenomena, Vol.214, No.1, pp.67-74, 2014.

Nima Gorjian, Lin Ma, Murthy Mittinty, et al., "A review on degradation models in reliability analysis", Proceedings of the 4th World Congress on Engineering Asset Management, Athens, Greece, USA, pp.28-30, 2009.

W. Huang and DL Dietrich, "An alternative degradation reliability modeling approach using maximum likelihood estimation", IEEE Transactions on Reliability, Vol.54, No.2, pp.310-317, 2005.

G.X. Sun, et al., "A stochastic degradation modeling based adaptive prognostic approach for equipment", Chinese Journal of Electronics, Vol.43, No.6, pp.1119-1126, 2015.

J.C. Lu and W.Q. Meeker, "Using degradation measures to estimate a time-to-failure distribution", Technometrics, Vol.35, No.2, pp.161-173, 1993.

Mu Seong Chang, Jung Hun Shin, Young Ⅱ Kwon, et al., "Reliability estimation of pneumatic cylinders using performance degradation data", International Journal of Precision Engineering and Manufacturing, Vol.14, No.12, pp.2081-2086, 2013.

Chien-Yu Peng and Sheng-Tsaing Tseng, "Progressive-stress accelerated degradation test for highly-reliable products", IEEE Transactions on Reliability, Vol.59, No.1, pp.30-37, 2010.

Laidler, Keith, "The development of the Arrhenius equation", Journal of Chemical Education, Vol.61, No.6, pp.494-498, 1984.

Min-Te Chao, "Degradation analysis and related topics:Some thoughts and a review", Proceedings of the National Science Council, Part A, Vol.23, No.5, pp.555-566, 1999.

José R. Celaya, Abhinav Saxena and Chetan S. Kulkarni, "Prognostics approach for power MOSFET under thermal-stress aging", Proceedings of Reliability and Maintainability Symposium (RAMS), Reno, NV, Jan., pp.1-6, 2012.

J.Y. Cai, "Research on analysis and assessment of systemic performance reliability for electronic equipment", Ph.D. Thesis, Nanjing University of Science & Technolog, China, 2010. (in Chinese)

S.L. Zhang, et al., "Variable selection in logistic regression model", Chinese Journal of Electronics, Vol.24, No.4, pp.813-817, 2015.

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