陌生小样本不平衡数据下基于机器学习联合算法的设备寿命预测研究

针对设备寿命预测中出现的缺乏状态标签以及数据样本匮乏、分布不平衡的问题,提出了基于PSO的改进K-means算法与一套基于传统SMOTE的数据优化方案.在优化K-means算法的过程中联合粒子群算法的特点,通过给定粒子群算法粒子生成范围以提高粒子群算法的寻优效率,从而快速判断设备所处的工作状态,再通过比较同簇样本距离均值与样本到中心点的距离建立改进SMOTE算法,通过新增少数类样本个数以规避样本不平衡带来的计算误差.最后利用AdaBoost集成优化KNN算法提升分类效果并通过拟合出设备寿命曲线,从而更好地预测设备健康水平与未来寿命情况.算例证明,该模型可以有效预测小样本不平衡数据下设备的健康状态.

Research on equipment life prediction based on machine learning combined algorithm under unknown small sample unbalanced data

In order to solve the problems of lack of state labels, lack of data samples and unbalanced distribution in equipment life prediction, this paper proposed an improved K-means algorithm based on PSO and a data optimization scheme based on traditional SMOTE. In the process of optimizing K-means algorithm, it combined the characteristics of particle swarm optimization algorithm, and improved the optimization efficiency of particle swarm optimization algorithm by giving the particle generation range of particle swarm optimization algorithm, so as to quickly judge the working state of the equipment. Then, by comparing the mean distance of samples in the same cluster with the distance from the sample to the center, it established an improved SMOTE algorithm, and avoided the calculation error caused by the imbalance of samples by adding the number of minority samples. Finally, this paper used Adaboost integrated optimization KNN algorithm to improve the classification effect, and by fitting the equipment life curve, it could better predict the equipment health level and future life. The example shows that the proposed model can effectively predict the health status of equipment under small sample unbalanced data.