Adams, C., & Neely, A., 2000. The performance prism to boost M&A success. Measuring business excellence.
Bagdonavičius, V., & Nikulin, M., 2009. Statistical models to analyze failure, wear, fatigue, and degradation data with explanatory variables. Communications in Statistics—Theory and Methods, 38(16-17), 3031-3047.
Barlas, Y., & Carpenter, S., 1990. Philosophical roots of model validation: two paradigms. System Dynamics Review, 6(2), 148-166.
Ben-Daya, M., Kumar, U., & Murthy, D. P., 2016. Introduction to maintenance engineering: modelling, optimization and management. John Wiley & Sons.
Braun, V., & Clarke, V., 2019. Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589-597.
Chen, N., Ye, Z. S., Xiang, Y., & Zhang, L., 2015. Condition-based maintenance using the inverse Gaussian degradation model. European Journal of Operational Research, 243(1), 190-199.
https://doi.org/10.1016/j.ejor.2014.11.029
Chookah, M., Nuhi, M., & Modarres, M., 2011. A probabilistic physics-of-failure model for prognostic health management of structures subject to pitting and corrosion-fatigue. Reliability Engineering & System Safety, 96(12), 1601-1610.
https://doi.org/10.1016/j.ress.2011.07.007
De Bruijn, H., Größler, A., & Videira, N., 2020. Antifragility as a design criterion for modelling dynamic systems. Systems Research and Behavioral Science, 37(1), 23-37.
Derbyshire, J., & Wright, G., 2014. Preparing for the future: development of an ‘antifragile’methodology that complements scenario planning by omitting causation. Technological Forecasting and Social Change, 82, 215-225.
Eleuteri, A., Tagliaferri, R., Milano, L., De Placido, S., & De Laurentiis, M., 2003. A novel neural network-based survival analysis model. Neural Networks, 16(5-6), 855-864.
Gaur, V., Yadav, O. P., Soni, G., & Rathore, A. P. S., 2019. A review of metrics, algorithms and methodologies for network reliability. In 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 1129-1133). IEEE.
Gebraeel, N., Elwany, A., & Pan, J., 2009. Residual life predictions in the absence of prior degradation knowledge. IEEE Transactions on Reliability, 58(1), 106-117.
Gopal K.Kanji, 1998. Measurement of business excellence, Total Quality Management, 9:7, 633-643, https://doi.org/10.1080/0954412988325
Gorjian N., Ma L., Mittinty M., Yarlagadda P., Sun Y., 2010. A review on degradation models in reliability analysis. In: Kiritsis D., Emmanouilidis C., Koronios A., Mathew J. (eds) Engineering Asset Lifecycle Management. Springer, London.
Guest, G., MacQueen, K. M., & Namey, E. E., 2012. Introduction to applied thematic analysis. Applied thematic analysis, 3(20), 1-21.
Hao Peng, Qianmei Feng & David W. Coit, 2010. Reliability and maintenance modeling for systems subject to multiple dependent competing failure processes, IIE Transactions, 43:1, 12-22.
https://doi.org/10.1080/0740817X.2010.491502
Kaplan, R. S., & Norton, D. P., 1992. The balanced scorecard--measures that drive performance. Harvard Business Review, 70(1), 71-79.
Keedy, E., & Feng, Q., 2012. A physics-of-failure based reliability and maintenance modeling framework for stent deployment and operation. Reliability Engineering & System Safety, 103, 94-101.
Kjell A. Doksum & Arnljot Hbyland, 1992. Models for Variable-Stress Accelerated Life Testing Experiments Based on Wener Processes and the Inverse Gaussian Distribution, Technometrics, 34:1, 74 82.
Lawless, J., & Crowder, M., 2004. Covariates and random effects in a gamma process model with application to degradation and failure. Lifetime data analysis, 10(3), 213-227.
Li, C., Tao, L., & Yongsheng, B. , 2007. Condition residual life evaluation by support vector machine. In 2007 8th International Conference on Electronic Measurement and Instruments (pp. 4-441). IEEE.
Lu, C. J., & Meeker, W. O., 1993. Using degradation measures to estimate a time-to-failure distribution. Technometrics, 35(2), 161-174.
Ma, K., Wang, H., & Blaabjerg, F., 2016. New approaches to reliability assessment: Using physics-of-failure for prediction and design in power electronics systems. IEEE Power Electronics Magazine, 3(4), 28-41.
https://doi.org/10.1109/MPEL.2016.2615277
Mohsenijam, A., Siu, M. F. F., & Lu, M., 2017. Modified stepwise regression approach to streamlining predictive analytics for construction engineering applications. Journal of Computing in Civil Engineering, 31(3), 04016066.
https://doi.org/10.1061/(ASCE)CP.1943-5487.0000636
Ren, Y., Cui, B., Feng, Q., Yang, D., Fan, D., Sun, B., & Li, M., 2020. A reliability evaluation method for radial multi-microgrid systems considering distribution network transmission capacity. Computers & Industrial Engineering, 139, 106145.
Saidi, L., Ali, J. B., Bechhoefer, E., & Benbouzid, M., 2017. Wind turbine high-speed shaft bearings health prognosis through a spectral Kurtosis-derived indices and SVR. Applied Acoustics, 120,1-8.
Shin, I., Lee, J., Lee, J. Y., Jung, K., Kwon, D., Youn, B. D., ... & Choi, J. H., 2018. A framework for prognostics and health management applications toward smart manufacturing systems. International Journal of Precision Engineering and Manufacturing-Green Technology, 5(4), 535-554.
Song, Y., Liu, D., Yang, C., & Peng, Y. , 2017. Data-driven hybrid remaining useful life estimation approach for spacecraft lithium-ion battery. Microelectronics Reliability, 75, 142-153.
https://doi.org/10.1016/j.microrel.2017.06.045
Sterman, J. D., 2000. Business dynamics: System thinking and modeling for a complex world Irwin McGraw-Hill. Massachusetts Institute of Technology, Engineering Systems Division: Cambridge, MA, USA.
Sterman, J. D., 2001. System dynamics modeling: tools for learning in a complex world. California management review, 43(4), 8-25.
https://doi.org/10.2307/41166098
Taleb, N. N., 2010. The Black Swan. The Impact of the Highly Improbable. Random House Incorporated.
Taleb, N. N., 2012. Antifragile: Things that gain from disorder. Random House Incorporated.
Tseng, S. T., & Peng, C. Y., 2007. Stochastic diffusion modeling of degradation data. Journal of data Science, 5(3), 315-333.
Wang, Y., & Pham, H., 2012. Modeling the dependent competing risks with multiple degradation processes and random shock using time-varying copulas. IEEE Transactions on Reliability, 61(1), 13-22. [6045314].
Yadav, O. P., Choudhary, N., & Bilen, C., 2008. Complex system reliability estimation methodology in the absence of failure data. Quality and reliability engineering international, 24(7), 745-764.
https://doi.org/10.1002/qre.920
Yu, J., Zheng, S., Pham, H., & Chen, T., 2018. Reliability modeling of multi‐state degraded repairable systems and its applications to automotive systems. Quality and Reliability Engineering International, 34(3), 459-474.
Yu, W., Wen, K., Min, Y., He, L., Huang, W., & Gong, J., 2018. A methodology to quantify the gas supply capacity of natural gas transmission pipeline system using reliability theory. Reliability Engineering & System Safety, 175, 128-141.
https://doi.org/10.1016/j.ress.2018.03.007
Zhang, F., & Shi, Y., 2020. Geometry on the statistical manifold induced by the degradation model with soft failure data. Journal of Computational and Applied Mathematics, 363, 211-222.
https://doi.org/10.1016/j.cam.2019.06.003
Zhang, X., Wang, B., & Chen, X., 2015. Intelligent fault diagnosis of roller bearings with multivariable ensemble-based incremental support vector machine. Knowledge-Based Systems, 89, 56-85
https://doi.org/10.1016/j.knosys.2015.06.017
Zhou, Q., & Thai, V. V., 2016. Fuzzy and grey theories in failure mode and effect analysis for tanker equipment failure prediction. Safety science, 83, 74-79.
Zhu, S. P., Huang, H. Z., Peng, W., Wang, H. K., & Mahadevan, S., 2016. Probabilistic physics of failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty. Reliability Engineering & System Safety, 146, 1-12.
https://doi.org/10.1016/j.ress.2015.10.002
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