China Academy of Launch Vehicle Technology, Beijing 100076, China.
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China.
Sensors (Basel). 2020 Mar 19;20(6):1710. doi: 10.3390/s20061710.
Sensor selection plays an essential and fundamental role in prognostics and health management technology, and it is closely related to fault diagnosis, life prediction, and health assessment. The existing methods of sensor selection do not have an evaluation standard, which leads to different selection results. It is not helpful for the selection and layout of sensors. This paper proposes a comprehensive evaluation method of sensor selection for prognostics and health management (PHM) based on grey clustering. The described approach divides sensors into three grey classes, and defines and quantifies three grey indexes based on a dependency matrix. After a brief introduction to the whitening weight function, we propose a combination weight considering the objective data and subjective tendency to improve the effectiveness of the selection result. Finally, the clustering result of sensors is obtained by analyzing the clustering coefficient, which is calculated based on the grey clustering theory. The proposed approach is illustrated by an electronic control system, in which the effectiveness of different methods of sensor selection is compared. The result shows that the technique can give a convincing analysis result by evaluating the selection results of different methods, and is also very helpful for adjusting sensors to provide a more precise result. This approach can be utilized in sensor selection and evaluation for prognostics and health management.
传感器选择在预测和健康管理技术中起着至关重要和基础的作用，它与故障诊断、寿命预测和健康评估密切相关。现有的传感器选择方法没有评估标准，导致选择结果不同，对传感器的选择和布局没有帮助。本文提出了一种基于灰色聚类的预测和健康管理（PHM）传感器选择的综合评价方法。该方法将传感器分为三个灰色类，并基于依赖矩阵定义和量化三个灰色指标。在简要介绍白化权函数后，我们提出了一种综合考虑客观数据和主观趋势的组合权重，以提高选择结果的有效性。最后，通过分析基于灰色聚类理论的聚类系数得到传感器的聚类结果。通过电子控制系统说明了该方法，并比较了不同传感器选择方法的效果。结果表明，该技术可以通过评估不同方法的选择结果给出令人信服的分析结果，对调整传感器以提供更精确的结果也很有帮助。该方法可用于预测和健康管理中的传感器选择和评估。
