Remaining useful life re-prediction methodology based on Wiener process: Subsea Christmas tree system as a case study
Remaining useful life re-prediction methodology based on Wiener process: Subsea Christmas tree system as a case study
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基于维纳过程的剩余使用寿命重新预测方法——以海底圣诞树系统为例
DOI：
10.1016/j.cie.2020.106983
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发表时间：
2021-02-04
影响因子：
7.9
通讯作者：
Ji, Renjie
中科院分区：
文献类型：
--
作者：
Cai, Baoping;Fan, Hongyan;Ji, Renjie
随着系统复杂性和综合水平的不断提高，其可靠性变得越来越重要。利用随机效应退化模型描述系统退化过程的剩余使用寿命估计方法已得到广泛应用，如维纳过程。然而，传统的基于维纳过程的退化模型只考虑当前的监测数据，而不是历史的退化数据，这导致了RUL预测的不准确性。此外，在工程应用中，由于传感器网络、系统生命周期长等特点，往往会出现数据丢失的情况，导致应用效果不理想。本文提出了一种结合系统当前监测状态和历史退化数据的基于维纳过程的RUL重预测方法。在初始预测过程中，采用Wiener过程描述系统的退化过程，采用期望最大化算法（EM算法）估计漂移系数和扩散系数，建立系统性能退化的动态贝叶斯网络（DBNs）模型，解决数据缺失带来的不确定性问题。在再预测过程中，结合n组性能退化监测数据和历史预测数据，计算出Wiener过程各阶段的基本退化情况，并利用DBNs进行建模。RUL值由检测点与预测故障点之间的时间差得到，最终由故障阈值确定。最后以水下采油树系统为例进行了验证。
With the continuous improvement of the complexity and comprehensive level of the system, its reliability becomes more and more important. The remaining useful life (RUL) estimation method using the degradation model with random effect to describe the degradation process of the system has been widely used such as Wiener process. However, the conventional Wiener-process-based degradation model only considers the current monitoring data but not the historical degradation data, which leads to the inaccuracy of RUL prediction. Furthermore, in engineering, there will always be data missing caused by sensor networks, long life cycle properties of system and so on, leading to unsatisfactory results. This paper contributed a RUL re-prediction method based on Wiener process combining the current monitoring status and historical degradation data of the system. In the initial prediction process, the Wiener process is used to describe the degradation process of the system, the drift coefficient and diffusion coefficient are estimated by Expectation Maximization algorithm (EM algorithm), and the dynamic Bayesian networks (DBNs) model for system performance degradation is established to solve the uncertainty caused by missing data. In the re-prediction process, n groups of performance degradation monitoring data and historical predicted data are combined to calculate the basic degradation in each stage of Wiener process, and the DBNs are used for modeling. The RUL value is obtained by the time difference between the detection point and the predicted fault point, it is determined by the failure threshold finally. A case of subsea Christmas tree system is adopted to demonstrate the proposed approach.