Actually, it is difficult to obtain a large number of sample data due to equipment failure, and small sample data may also be missing. This paper proposes a novel small sample data missing filling method based on support vector regression (SVR) and genetic algorithm (GA) to improve equipment health diagnosis effect. First, the genetic algorithm is used to optimize support vector regression, and a new method GA-SVR can be proposed. The GA-SVR model is trained by using other data of the variable to which the missing data belongs, and the single-variable prediction method can be obtained. The correlation analysis is used to reconstruct the training set, and the GA-SVR is trained by using the data of the variables related to the missing data to obtain the multivariate prediction method. Then, the dynamic weight is presented to combine the single-variable prediction method with the multiple-variable prediction method based on certain principles, and the missing data are filled with the combined prediction methods. The filled data are used as input of GA-SVM to diagnose equipment failure. Finally, a case study is given to verify the applicability and effectiveness of the proposed method.

中文翻译：

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缺少小样本数据的设备健康诊断的支持向量回归新模型及其应用

实际上，由于设备故障而难以获取大量样本数据，并且可能还会丢失少量样本数据。提出了一种基于支持向量回归（SVR）和遗传算法（GA）的小样本数据缺失填充方法，以提高设备的健康诊断效果。首先，利用遗传算法对支持向量回归进行优化，提出了一种新的GA-SVR方法。通过使用缺失数据所属的变量的其他数据来训练GA-SVR模型，可以获得单变量预测方法。利用相关分析重建训练集，利用与缺失数据相关的变量数据对GA-SVR进行训练，得到多元预测方法。然后，提出了基于某些原理的动态权重，将单变量预测方法与多变量预测方法相结合，并用组合预测方法填充了缺失数据。填充的数据用作GA-SVM的输入，以诊断设备故障。最后，通过案例研究验证了该方法的适用性和有效性。