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Reliability Analysis based on Inverse Gauss Degradation Process and Evidence Theory

Volume 15, Number 2, February 2019, pp. 353-361
DOI: 10.23940/ijpe.19.02.p1.353361

Yuwei Wang and Hailin Feng

School of Mathematics and Statistics, Xidian University, Xi’an, 710126, China

(Submitted on October 16, 2018; Revised on November 17, 2018; Accepted on December 15, 2018)

Abstract:

The degradation analysis of products has been demonstrated as a significant toolkit for reliability analysis. Data from the same batch of products in different working environments cannot be directly used to analyze product reliability. In this paper, motivated by this circumstance, we first assume that degradation data sets from different working environments are subject to different inverse Gaussian process models, and maximum likelihood estimation is used to obtain multiple model parameters. Secondly, we construct evidence by quantifying different information of products, apply the evidence theory to fuse model parameters, and then analyze the reliability of products from the same batch. Finally, we use performance degradation data of the laser to illustrate the method.

 

References: 17

        1. X. R. Cheng and J. Y. Li, “Remaining Lifetime Prediction of Blowout Preventer Valve based on Fusion of Lifetime Data and Degradation Data,” in Proceedings of Journal of Shandong University of Science and Technology, Vol. 36, No. 5, pp. 23-28, 2017
        2. L. S. Khanh and F. A. B. Mitra, “Remaining Useful Lifetime Estimation and Noisy Gamma Deterioration Process,” in Proceedings of Reliability Engineering and System Safety, Vol. 149, pp. 76-87, 2016
        3. Z. S. Ye and N. Chen, “The Inverse Gaussian Process as a Degradation Model,” Proceedings of Technometrics, Vol. 56, No. 3, pp. 302-311, 2014
        4. F. Duan and G. Wang, “Reliability Modeling of Two-Phase Inverse Gaussian Degradation Process,” in Proceedings of the Second International Conference on Reliability Systems Engineering, IEEE, pp. 1-6, 2017
        5. H. Guo, T. Zhang, L. Y. Ping, and E. S. Pan, “Research on Competing Failure Modeling based on the Inverse Gaussian Process,” in Proceedings of Industrial Engineering and Management, Vol. 22, No. 1, pp. 89-94, 2017
        6. J. B. Liu, D. H. Pan, and J. Cao, “Remaining Useful Life Estimation using an Inverse Gaussian Degradation Model,” Neurocomputing, Vol. 185, pp. 64-72, 2016
        7. Y. Zhou, L. V. Wei-Min, and Y. Sun, “Fusion Prediction Method for the Life of MEMS Accelerometer based on Inverse Gaussian Process,” Journal of Chinese Inertial Technology, 2017
        8. W. Peng, Y. J. Yang, J. Mi and H. Z. Huang, “Bayesian Degradation Analysis with Inverse Gaussian Process Models under Time Varying Degradation Rates,” IEEE Transactions on Reliability, No. 99, pp. 1-13, 2017
        9. X. Zhang, Y. Li, and X. Wang, “Maintenance Strategy of Corroded Oil-Gas Pipeline based on Inverse Gaussian Process,” in Proceedings of Acta Petrolei Sinica, Vol. 38, No. 3, pp. 356-362, 2017
        10. W. Peng, Y. F. Li, Y. J. Yang, S. P. Zhu, and H. G. Huang, “Bivariate Analysis of Incomplete Degradation Observations based on Inverse Gaussian Processes and Copulas,” IEEE Transactions on Reliability, Vol. 65, No. 2, pp. 624-639, 2016
        11. F. Ye, J. Chen, and Y. Li, “Improvement of D-S Evidence Theory for Multi-Sensor Conflicting Information,” Symmetry, 2017
        12. J. B. Yang and M. G. Singh, “An Evidential Reasoning Approach for Multiple Attribute Decision Making with Uncertainty,” IEEE Transactions on Systems Man and Cybernetics, Vol. 22, No. 1, pp. 1-18, 1994
        13. Z. Zhang, C. Jiang, X. X. Ruan, and F. J. Guan, “A Novel Evidence Theory Model Dealing with Correlated Variables and the Corresponding Structural Reliability Analysis Method,” Structural & Multidisciplinary Optimization, No. 1-3, pp. 1-16, 2017
        14. L. F. Ming, C. H. Hu, Z. J. Zhou, and P. Wang, “A degradation Modeling Method based on Inverse Gaussian Process and Evidential Reasoning,” in Proceedings of Electronics Optics & Control, Vol. 22, No. 1, pp. 92-96, 2015
        15. L. Liang, Y. Shen, Q .Cai, and Y. Gu, “A Reliability Data Fusion Method based on Improved D-S Evidence Theory,” in Proceedings of International Conference on Reliability, Maintainability and Safety, IEEE, pp. 1-6, 2017
        16. H. Wang, G. J. Wang, and F. J. Duan, “Planning of Step-Stress Accelerated Degradation Test based on the Inverse Gaussian Process,” Reliability Engineering & System Safety, Vol. 154, pp. 97-105, 2016
        17. D. Dubois and H. Prade, “A Survey of Belief Revision and Updating Rules in Various Uncertainty Models,” Hoboken, John Wiley & Sons, 1994

               

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