基于主元数据的大型复杂机电设备寿命预测方法

针对单一表征参数的大型复杂机电设备寿命预测方法无法全面表征设备退化轨迹以及多表征参数之间存在相关性从而影响寿命预测准确性的问题，提出一种基于表征参数数据融合的大型复杂机电设备的寿命预测方法。先提出一种加权主成分分析法，对多种表征设备退化的参数进行融合，得到可以表征设备退化且相互之间不存在相关性的主元数据；然后，建立基于主元数据的维纳退化过程模型；最后，选取数控机床进给系统的双向定位精度、双向重复定位精度、反向间隙误差、直线度4种表征参数对应的数据对模型进行了具体的分析和验证。试验结果表明：与基于单一表征参数和多表征参数的寿命预测模型相比，融合后的主元参数能够更好地表征设备的退化过程，从而可以更精确地实现设备的剩余寿命估计。

Lifetime Prediction Method for Large-scale Complex Electromechanical Equipment Based on Fusion Data

To solve the problem that a single characterization parameter could not express the paths of degradation completely and the correlation between multiple parameters could cause a bad effect,a life prediction method based on master metadata for large-scale complex electromechanical equipment was presented. Firstly, mutual information from multiple features was fused by using the method of principal component analysis, and the master metadata that could characterize the equipment degradation and had no correlation with each other were obtained. Then, a Wiener degradation process model based on the master metadata was constructed. Finally, a specific analysis and verification was made by using the data adopted from NC machine tool.The examination identifies the effectiveness of the proposed method in large-scale complex electromechanical equipment by eliminating the correlation between the characterization parameters.