| Citation: | YANG J X，TANG S J，LI L，et al. Remaining useful life prediction based on implicit nonlinear Wiener degradation process[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（1）：328-340 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0243
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Accurate remaining useful life prediction helps to improve the reliability and safety of the system and reduce the economic cost of the whole life cycle of the system. In engineering applications, due to the influence of uncertain measurement, the nonlinear degradation characteristics of stochastic degradation systems are in an implicit state. Since lifetime distribution estimation and degradation modeling are now the two main applications for the implicit scale transformation nonlinear Wiener degradation process, a remaining useful life prediction approach for this process is provided in this study. The parameters are updated online in accordance with the field degradation data of the equipment by the Kalman filtering approach, after the degradation model based on the nonlinear Wiener process is constructed and takes into account both measurement errors and nonlinear deterioration through scale transformation. The analytical expression of the probability density function and cumulative distribution function of remaining useful life considering online updating of model parameters are derived. Then, based on the historical degradation data, a maximum likelihood unbiased estimation method for the unknown parameters of the implicit scale transformation nonlinear Wiener degradation process model is proposed. The simulation degradation data and actual turbofan engine data are used for experimental verification. The experimental results show that the remaining useful life prediction method considering both measurement uncertainty and nonlinear degradation characteristics obtains higher prediction accuracy.
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