A deep belief network based health indicator construction and remaining useful life prediction using improved particle filter
A deep belief network based health indicator construction and remaining useful life prediction using improved particle filter
复制标题
基于深度信念网络的健康指标构建和使用改进的粒子过滤器的剩余使用寿命预测
DOI：
10.1016/j.neucom.2019.07.075
复制
发表时间：
2019-10-07
期刊：
影响因子：
6
通讯作者：
Pi, Yanting
中科院分区：
文献类型：
--
作者：
Peng, Kaixiang;Jiao, Ruihua;Pi, Yanting
剩余使用寿命的预测在结构和健康管理中具有重要的意义，可以为维修提供有益的参考。健康指标的构建是预测的重要组成部分，合适的健康指标可以反映系统的退化程度，为系统剩余使用寿命的估算提供有用的信息。提出了一种基于深度信念网络的无监督健康指标构建方法，并将其与粒子滤波相结合进行剩余使用寿命预测。该方法首先训练深度信念网络提取系统故障状态对应的隐含特征，并利用退化状态与失效状态之间的距离构造健康指标。然后，通过引入模糊推理系统改进粒子滤波算法，预测失效前的剩余使用寿命。最后，利用航空发动机数据集进行了算例分析，结果表明，该方法的预测精度优于传统方法。(C)2019爱思唯尔B.V.保留所有权利。
The prediction of remaining useful life plays a significant role in prognostics and health management, which can give helpful reference for maintenance. The construction of health indicator is an important part of prediction, and a suitable health indicator can reflect the degradation degree of the system and provide useful information for remaining useful life estimation. This paper presents an unsupervised health indicator construction method based on deep belief network and combines it with particle filter for remaining useful life prediction. Firstly, deep belief network is trained to extract the hidden characteristics corresponding to fault state of a system, and the distance between degraded state and failed state is used to construct health indicator. Afterwards, the remaining useful life before failure is predicted by particle filter which is improved by introducing a fuzzy inference system. At last, a case study using aircraft engine dataset is performed to verify the effectiveness of the proposed method, and the results show that the proposed method gives better prediction accuracy compared to traditional methods. (C) 2019 Elsevier B.V. All rights reserved.