A Model-Based Method for Remaining Useful Life Prediction of Machinery
A Model-Based Method for Remaining Useful Life Prediction of Machinery
复制标题
基于模型的机械剩余使用寿命预测方法
DOI：
10.1109/tr.2016.2570568
复制
发表时间：
2016-09-01
影响因子：
5.9
通讯作者：
Dybala, Jacek
中科院分区：
文献类型：
--
作者：
Lei, Yaguo;Li, Naipeng;Dybala, Jacek
剩余使用寿命（RUL）预测允许对机器进行预测性维护，从而减少昂贵的计划外维护。因此，机械产品的RUL预测成为一个备受关注的热点问题，同时也是一个极具挑战性的问题。本文提出了一种基于模型的方法来预测机器的RUL。该方法包括两个模块，指标构建和RUL预测。在第一个模块中，构造了一个新的健康指标加权最小量化误差，它融合了多个特征的互信息，并适当地与机械的退化过程相关。在第二模块中，使用最大似然估计算法初始化模型参数，并使用基于粒子滤波的算法预测RUL。所提出的方法证明了使用振动信号的滚动轴承加速退化试验。预测结果验证了所提出的方法在预测机械RUL方面的有效性。
Remaining useful life (RUL) prediction allows for predictive maintenance of machinery, thus reducing costly unscheduled maintenance. Therefore, RUL prediction of machinery appears to be a hot issue attracting more and more attention as well as being of great challenge. This paper proposes a model-based method for predicting RUL of machinery. The method includes two modules, i.e., indicator construction and RUL prediction. In the first module, a new health indicator named weighted minimum quantization error is constructed, which fuses mutual information from multiple features and properly correlates to the degradation processes of machinery. In the second module, model parameters are initialized using the maximum-likelihood estimation algorithm and RUL is predicted using a particle filtering-based algorithm. The proposed method is demonstrated using vibration signals from accelerated degradation tests of rolling element bearings. The prediction result identifies the effectiveness of the proposed method in predicting RUL of machinery.