References
- Adcock, C. J., Ye, C., Yin, S., & Zhang, D. (2019). Price discovery and volatility spillover with price limits in Chinese A-shares market: A truncated GARCH approach. Journal of the Operational Research Society, 70(10), 1709–1719. https://doi.org/10.1080/01605682.2018.1542973
- Ahmadi, R. (2020). A new approach to maintenance optimisation of repairable parallel systems subject to hidden failures. Journal of the Operational Research Society, 71(9), 1448–1465. https://doi.org/10.1080/01605682.2019.1614490
- Basir, O., & Yuan, X. (2007). Engine fault diagnosis based on multi-sensor information fusion using DempsterCShafer evidence theory. Information Fusion, 8(4), 379–386. https://doi.org/10.1016/j.inffus.2005.07.003
- Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1
- Chen, S., Härdle, W., & Jeong, K. (2009). Forecasting volatility with support vector machine-based GARCH model. Journal of Forecasting, 29, n/a–433. https://doi.org/10.1002/for.1134
- Desbazeille, M., Randall, R. B., Guillet, F., Badaoui, M. E., & Hoisnard, C. (2010). Model-based diagnosis of large diesel engines based on angular speed variations of the crankshaft. Mechanical Systems and Signal Processing, 24(5), 1529–1541. https://doi.org/10.1016/j.ymssp.2009.12.004
- Gerlach, R., & Tuyl, F. (2006). MCMC methods for comparing stochastic volatility and GARCH models. International Journal of Forecasting, 22(1), 91–107. https://doi.org/10.1016/j.ijforecast.2005.04.020
- Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance, 48(5), 1779–1801. https://doi.org/10.1111/j.1540-6261.1993.tb05128.x
- Ho, S. L., & Xie, M. (1998). The use of ARIMA models for reliability forecasting and analysis. Computers & Industrial Engineering, 35(1–2), 213–216. https://doi.org/10.1016/S0360-8352(98)00066-7
- Hountalas, D. T. (2000). Prediction of marine diesel engine performance under fault conditions. Applied Thermal Engineering, 20(18), 1753–1783. https://doi.org/10.1016/S1359-4311(00)00006-5
- Li, G., Yang, L., Lee, C. G., Wang, X., & Rong, M. (2021). A Bayesian deep learning RUL framework integrating epistemic and aleatoric uncertainties. IEEE Transactions on Industrial Electronics, 68(9), 8829–8841. https://doi.org/10.1109/TIE.2020.3009593
- Li, L., You, M. Y., & Ni, J. (2009). Reliability-based dynamic maintenance threshold for failure prevention of continuously monitored degrading systems. Journal of Manufacturing Science and Engineering, 131(3), 1010–1018. https://doi.org/10.1115/1.3123340
- Li, Z. X., Yan, X. P., Yuan, C. Q., & Peng, Z. X. (2012). Intelligent fault diagnosis method for marine diesel engines using instantaneous angular speed. Journal of Mechanical Science and Technology, 26(8), 2413–2423. https://doi.org/10.1007/s12206-012-0621-2
- Liu, F., Fang, K., Tang, J., & Yin, Y. (2021). Solving the rotating seru production problem with dynamic multi-objective evolutionary strategies. Journal of Management Science and Engineering, forthcoming. https://doi.org/10.1016/j.jmse.2021.05.004
- Pham, H. T., & Yang, B. S. (2010). Estimation and forecasting of machine health condition using ARMA/GARCH model. Mechanical Systems and Signal Processing, 24(2), 546–558. https://doi.org/10.1016/j.ymssp.2009.08.004
- Taylor, W. T., & Jeon, J. (2018). Probabilistic forecasting of wave height for offshore wind turbine maintenance. European Journal of Operational Research, 267(3), 877–890. https://doi.org/10.1016/j.ejor.2017.12.021
- Xiahou, T. F., Zeng, Z. G., & Liu, Y. (2021). Remaining useful life prediction by fusing experts’ knowledge and condition monitoring information. IEEE Transactions on Industrial Informatics, 17(4), 2653–2663. https://doi.org/10.1109/TII.2020.2998102
- Xiao, H., Yi, K. X., Kou, G., & Xing, L. D. (2020). Reliability of a two-dimensional demand-based networked system with multistate components. Naval Research Logistics (NRL), 67(6), 453–468. https://doi.org/10.1002/nav.21922
- Zhao, F., Wang, W. B., & Peng, R. (2015). Delay-time-based preventive maintenance modelling for a production plant: A case study in a steel mill. Journal of the Operational Research Society, 66(12), 2015–2024. https://doi.org/10.1057/jors.2015.20
- Zhao, X. F., Cai, J. J., Mizutani, S., & Nakagawa, T. (2020a). Preventive replacement policies with time of operations, mission durations, minimal repairs and maintenance triggering approaches. Journal of Manufacturing Systems, 61, 819–829. https://doi.org/10.1016/j.jmsy.2020.04.003
- Zhao, X. F., Chen, M., & Nakagawa, T. (2020b). Periodic replacement policies with shortage and excess costs. Annals of Operations Research, https://doi.org/10.1007/s10479-020-03566-z