Integrate Uncertainty in the Process of Prognostics for Electronics

Article Preview

Abstract:

Elements of uncertainty in the electronics Prognostics process were studied. A method for electronics Dynamic Damage Optimal Estimation and prognostics based on Particle Filtering were proposed. Under the effect of time stress, the electronics cumulative damage is the result of the continuous effect of the stress, as a result, a HMM based electronics dynamic damage model was built at first place, analytical results of uncertainties in the process of prognostics were given and thus a Bayesian based filter system was built. Bayesian Filter change the problem of uncertainty into an optimal estimation processes as a result, the optimal estimation was fetched by applying the particle filtering into the estimation. The experiment case study proved that the proposed method can eliminate the uncertainties caused by measurement and the system effectively and improve the RUL prediction accuracy.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

132-137

Citation:

Online since:

July 2011

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2011 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Jie Gu, Michael Pecht. Prognostics and Health Management Using Physics-of-Failure[C]. 54th annual Reliability and Maintainability Symposium. Las Vegas, Nevada: IEEE, 2008, 262267.

DOI: 10.1109/rams.2008.4925843

Google Scholar

[2] Pecht Michael. Prognostics and Health Management of Electronics[M]. WILEY, 2008: 73-84.

Google Scholar

[3] Lv Ke Hong, Qiu Jing, Liu Guan Jun. Dynamic description model of the relationship between electromechanical system damage and time stress[J]. Chinese Journal of Scientific Instrument. 2008, 29(8): 17881792.

Google Scholar

[4] Lv Ke Hong, Qiu Jing, Liu Guan Jun. Research on Life Prognosis Method for Electronics Based on Dynamic Damage and Optimization AR Mode[J]. ACTA ARMAMENTARI. 2009, 30(1): 9195.

Google Scholar

[5] Sun Bo. Research on electronics fault prognostics and model[D]. Bei Jing: Beijing University of Aeronautics and Astronautic, (2007).

Google Scholar

[6] Fang Jiayong, Xian Mingqing, Huang Hongwei. Fault Prognosis Parameters Selection and Setting in Electronic Equipment[J]. Journal of Air Force Engineering University. 2010, 11(4): 1115.

Google Scholar

[7] Zeng Qinghu, Qiu Jing, Liu Guanjun. Reserch on equipment degradation state recognition and fault prognostics method based on KPCA-Hidden Semi-Markov Model[J]. Chinese Journal of Scientific Instrument. 2009, 30(7): 13411346.

DOI: 10.1109/wcica.2008.4592935

Google Scholar

[8] GU Jie. Prognostics of Solder Joint Reliability Under Vibration Loading Using Physics of Failure Approach[D]. Maryland: University of Maryland, (2009).

Google Scholar

[9] Vichare Nikhil M. Prognostics and Heath Management of Electronics By Utilizing Enviromental and Usage Loads[D]. Maryland: University of Maryland, (2006).

Google Scholar

[10] S. Mishra, S. Ganesa, M. Pecht. Life consumption monitoring for electronics prognostics[A]. : IEEE Press, 2004, 34553467.

Google Scholar

[11] Patrik W. Kalgren, Mark Baybutt, Antonio Ginart. Application of Prognostic Health Management in Digital Electronic Systems[A]. Big Sky: IEEE Press, 2007, 19.

DOI: 10.1109/aero.2007.352883

Google Scholar