基于WSVR和FCM聚类的实时寿命预测方法OA北大核心CSCDCSTPCD
Real-time Lifetime Prediction Method Based on Wavelet Support Vector Regression and Fuzzy c-means Clustering
针对产品的性能退化轨迹呈现为非线性特性,且个体的性能退化数据为小样本的情形,为了充分利用同类产品的性能退化数据进行特定个体的实时寿命预测,从研究退化轨迹相似性的角度出发,提出一类基于小波支持向量回归机(Wavelet support vector regression,WSVR)和模糊C均值(Fuzzy c-means,FCM)聚类的实时寿命预测方法.该方法分为离线和实时两个阶段:离线阶段先采用WSVR对同类产品的性能退化数据进行规范化处理,接着…查看全部>>
For the case where the products have nonlinear performance degradation paths and there is little performance degradation data for each individual, in order to take full advantage of performance degradation data of the same kind of products in individual real-time lifetime prediction, as viewed from the comparability of degradation paths, a class of real-time lifetime prediction methods are proposed, on the basis of wavelet support vector regression (WSVR) an…查看全部>>
胡友涛;胡昌华;孔祥玉;周志杰
第二炮兵工程大学自动化系 西安710025第二炮兵工程大学自动化系 西安710025第二炮兵工程大学自动化系 西安710025第二炮兵工程大学自动化系 西安710025
性能退化小波支持向量回归机模糊C均值聚类实时寿命预测
Performance degradationwavelet support vector regression (WSVR)fuzzy c-means (FCM) clusteringreal-time lifetime prediction
《自动化学报》 2012 (3)
一类基于数据驱动的复杂工程系统故障预测和可靠性评估
331-340,10
国家自然科学基金(60736026,61074072,61004069),国家杰出青年基金(61025014)资助
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