融合KPCA与信息粒化的滚动轴承性能退化SVM预测

doi:10.16182/j.issn1004731x.joss.201806043

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (6): 2345-2354.doi: 10.16182/j.issn1004731x.joss.201806043

融合KPCA与信息粒化的滚动轴承性能退化SVM预测

徐继亚, 王艳, 严大虎, 纪志成

江南大学物联网技术应用教育部工程研究中心,无锡 214122

收稿日期:2017-07-13 修回日期:2017-08-07 出版日期:2018-06-08 发布日期:2018-06-14
作者简介:徐继亚(1993-),男,山东枣庄,硕士生,研究方向为智能故障预测控制。
基金资助:
国家自然科学基金(61572238),江苏省杰出青年基金(BK20160001), 江苏省产学研联合创新资金-前瞻性联合研究项目(201602224)

SVM Prediction of Performance Degradation of Rolling Bearings with Fusion of KPCA and Information Granulation

Xu Jiya, Wang Yan, Yan Dahu, Ji Zhicheng

Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Wuxi 214122, China

Received:2017-07-13 Revised:2017-08-07 Online:2018-06-08 Published:2018-06-14

摘要/Abstract

摘要： 为了有效预测滚动轴承的性能退化指标及其波动范围,提出了一种基于融合核主元分析与模糊信息粒化的支持向量机预测方法。运用核主元分析对数据进行预处理,构造T²和SPE统计量,并分析其变化趋势。将统计量T²和SPE作为性能退化指标,并对其进行模糊信息粒化以提取有用信息。将粒化后的数据输入给支持向量机进行回归预测。实验结果表明,该预测方法能够有效跟踪性能退化指标的变化趋势及指标的波动范围。

关键词: 核主元分析, 模糊信息粒化, 支持向量机, 滚动轴承

Abstract: To effectively predict the performance degradation index and its fluctuation ranges of the rolling bearing, a prediction method based on kernel principal component analysis algorithm and fuzzy information granulation using support vector machine is proposed. The kernel principal component analysis is utilized to preprocess the data to acquire the main feature vector, construct T² and SPE statistics, and to analyze its trend. The statistical information is used as the performance degradation index. Theory of fuzzy information granulation is used to granulate the performance degradation index and extract the useful information. The granulated data are put to the support vector machine for regression prediction. The experiment results show the prediction method can track the change tendency of the performance degradation index of rolling bearing and the fluctuation range of its index effectively.

Key words: kernel principal component analysis, fuzzy information granulation, support vector machines, rolling bearing

中图分类号:

TP391.9

徐继亚, 王艳, 严大虎, 纪志成. 融合KPCA与信息粒化的滚动轴承性能退化SVM预测[J]. 系统仿真学报, 2018, 30(6): 2345-2354.

Xu Jiya, Wang Yan, Yan Dahu, Ji Zhicheng. SVM Prediction of Performance Degradation of Rolling Bearings with Fusion of KPCA and Information Granulation[J]. Journal of System Simulation, 2018, 30(6): 2345-2354.

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融合KPCA与信息粒化的滚动轴承性能退化SVM预测

SVM Prediction of Performance Degradation of Rolling Bearings with Fusion of KPCA and Information Granulation

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