Remaining useful lifetime prediction for equipment based on nonlinear implicit degradation modeling
Remaining useful lifetime prediction for equipment based on nonlinear implicit degradation modeling
复制标题
基于非线性隐式退化模型的设备剩余使用寿命预测
DOI：
10.21629/jsee.2020.01.19
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发表时间：
2020-02-01
影响因子：
2.1
通讯作者：
Xiang Huachun
中科院分区：
文献类型：
--
作者：
Cai Zhongyi;Wang Zezhou;Xiang Huachun
非线性和隐式是机械随机退化装置的常见退化特征。这些特征对设备的剩余使用寿命（RUL）预测具有不确定的影响。目前的数据驱动的RUL预测方法没有系统地研究非线性隐退化建模和RUL分布函数。本文利用非线性维纳过程建立了一个对偶非线性隐式退化模型。基于同类设备的历史实测数据，采用极大似然估计算法对固定系数和随机系数的先验分布进行估计。利用目标设备的现场实测数据，基于贝叶斯推理和扩展卡尔曼滤波算法，逐步更新随机系数的后验分布和实际退化状态。基于首次命中时间分布，导出了RUL分布函数的解析形式。结合两个算例，验证了该方法在预测精度上比现有方法有一定的优势。
Nonlinearity and implicitness are common degradation features of the stochastic degradation equipment for prognostics. These features have an uncertain effect on the remaining useful life (RUL) prediction of the equipment. The current data-driven RUL prediction method has not systematically studied the nonlinear hidden degradation modeling and the RUL distribution function. This paper uses the nonlinear Wiener process to build a dual nonlinear implicit degradation model. Based on the historical measured data of similar equipment, the maximum likelihood estimation algorithm is used to estimate the fixed coefficients and the prior distribution of a random coefficient. Using the on-site measured data of the target equipment, the posterior distribution of a random coefficient and actual degradation state are step-by-step updated based on Bayesian inference and the extended Kalman filtering algorithm. The analytical form of the RUL distribution function is derived based on the first hitting time distribution. Combined with the two case studies, the proposed method is verified to have certain advantages over the existing methods in the accuracy of prediction.