Abstract
In order to assess and predict the reliability of critical components, using performance degradation data for modeling has become a significant approach. In addition, degradation data often provides more information about the components’ performance. The degradation can be detected directly or indirectly by records of surveillance, inspection and check. The degradation increases with time in an uncertain manner and needs to be modelled as a continuous time stochastic process. A wiener process model for modeling the calibration data has been developed. The unknown parameters of the model are obtained by the maximum likelihood estimation approach. The approach is illustrated by a real world example. This resulting model is applied to predict the reliability measure in given longer calibration intervals. The reliability index can be an input for maintenance effectiveness assessment and also be a performance indicator in a system or component monitoring program, etc.
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A. Birolini, Reliability Engineering Theory and Practice, 5th edn., ch. 1 (Springer, New York, Berlin, Heidelberg, 2007), p. 2
W.Q. Meeker, N. Doganaksoy, G.J. Hahn, Using degradation data for product reliability analysis. Qual. Prog. 34(6), 60–65 (2001)
I.B. Gertsbackh, K.B. Kordonskiy, Models of Failure, ch. 3 (Springer, New York, 1969), pp. 86–90
M.J. Zuo, R.Y. Jiang, R.C.M. Yam, Approaches for reliability modeling of continuous-state devices. IEEE Trans. Reliab. 48(1), 9–18 (1999)
N. Gorjian, L. Ma, M. Mittinty, P. Yarlagadda, Y. Sun, A review on degradation models in reliability analysis, in Proceedings of the 4th Conference World Congress on Engineering Asset Management (Marriott Athens Ledra Hotel, Athens), pp. 28–30
H.B. Hao, C. Su, Z.Z. Qu, Reliability analysis for mechanical components subject to degradation process and random shock with wiener process, in Proceedings of the 19th Conference Industrial Engineering and Engineering Management (Springer, Berlin, Heidelberg), pp. 531–543
E. Davis, D. Funk, Technical report. Guidelines for instrument calibration extension/reduction—revision 1: statistical analysis of instrument calibration data. EPRI, Palo Alto, CA, 1998. TR-103335-R1
P. Zhou, C.H. Qiu, Q.B. Chu, Application of AFAL methodology in substation of instrument calibration intervals extension in nuclear power plant. Nucl. Power Eng. 34(5), 115–117 (2013) (in Chinese)
Y. Chen, L.H. Zhao, T. Yu, L.H. He, Z.J. Liu et al., Extension of instrument calibration intervals base on improved AFAL analysis. Nucl. Power Eng. 38(2), 64–67 (2017) (in Chinese)
R. Jiang, D.N.P. Murthy, Maintenance: Decision Models for Management (Science Press, Beijing, 2008), p. 102
Z. Sheng, S.Q. Xie, C.Y. Pan, Probability and Statistics (Higher Education Press, Beijing, 2008), p. 315 (in Chinese)
E.A. Elsayed, Reliability Engineering (Publishing House of Electronics Industry, Beijing, 2013), p. 287 (in Chinese)
R. Jiang, M.J. Zuo, Reliability Models and Application (China Machine Press, Beijing, 1999), p. 34 (in Chinese)
B.X. Dong, S.J. Wang, M.Y. Chen, G.F. Zhai, Reliability assessment method for aerospace relay based on wiener process. Elect. Energy Manage. Technol 11, 12–16 (2015)
ANSI N15.15-1974, Assessment of the assumption of normality (Employing Individual Observed Values)
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Liu, By., Qin, F., Liu, Sq., Cai, Sw. (2019). Reliability Modeling with Application for Calibration Data. In: Huang, G., Chien, CF., Dou, R. (eds) Proceeding of the 24th International Conference on Industrial Engineering and Engineering Management 2018. Springer, Singapore. https://doi.org/10.1007/978-981-13-3402-3_15
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DOI: https://doi.org/10.1007/978-981-13-3402-3_15
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