计算机工程与应用 ›› 2018, Vol. 54 ›› Issue (8): 149-153.DOI: 10.3778/j.issn.1002-8331.1612-0003

• 模式识别与人工智能 • 上一篇    下一篇

基于Lasso与RFE特征消除的RVM旋转机械故障预测

张媛媛1,原思聪1,郭田奇2   

  1. 1.西安建筑科技大学 机电工程学院,西安 710055
    2.兰州财经大学 统计学院,兰州 730000
  • 出版日期:2018-04-15 发布日期:2018-05-02

Fault prediction of RVM rotating machinery based on Lasso and RFE feature elimination

ZHANG Yuanyuan1, YUAN Sicong1, GUO Tianqi2   

  1. 1.School of Mechanical and Electrical Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
    2.School of Statistics, Lanzhou University of Finance and Economics, Lanzhou 730000, China
  • Online:2018-04-15 Published:2018-05-02

摘要: 针对旋转机械故障诊断问题,提出一种基于相关向量机(RVM)的故障检测方法,RVM是一种用于回归和分类问题的贝叶斯稀疏核方法,其突出的优势是模型的稀疏性和预测的概率性。为进一步提高RVM模型的鲁棒性,减小样本数据中异常值对预测值的影响,针对Lasso方法进行特征选择时无法去除冗余特征的问题,提出以Lasso为底层算法的RFE递归特征消除方法去除样本数据集中无关特征和冗余特征。最后以工业环境下采集的数据作为样本集进行实验,同传统算法进行了比较,结果表明该方法在保持较高检测率的同时,提高了故障预测的时效性和稳定性。

关键词: 旋转机械, 相关向量机, 故障诊断, 特征消除

Abstract: To solve the problem of rotating machinery fault diagnosis, a method based on correlation vector machine(RVM) is proposed, which is a Bayesian sparse kernel method for regression and classification. The prominent advantage of RVM is the sparseness of the model and the probability of prediction. In order to improve the robustness of RVM and reduce the influence of outliers on the predicted value, and solve the problem that the redundant feature can not be removed when Lasso method is used for feature selection, the RFE recursive feature elimination method using Lasso as the bottom algorithm is proposed to remove the irrelevant and redundant features of the sample data-set. Finally, the data collected in industrial environment is used as a sample set for testing and compared with the traditional algorithm, the results show that the method can improve the detection efficiency and maintain high detection rate.

Key words: rotating machinery, correlation vector machine, fault diagnosis, feature elimination