基于马氏距离和相关向量机的多变量退化预计
Mahalanobis Distance based Multivariate Degradation Prediction by Relevance Vector Machines
对于安全相关的产品,开展退化预测是非常重要的。当产品的多个参数都存在退化时,论文提出采用马氏距离(Mahalanobis distance)将多个参数组合为一个综合的指标。产品的健康限也是由这个综合的指标来确定的。最后，通过相关向量机（RVM）来预测综合指标的变化趋势并确定故障的时刻。论文通过一个例子来验证了所提的方法。
egradation prediction is important for safety related products to avoid failures. When the degradations of multiple parameters of a product is taken into account, Mahalanobis distance is proposed, to combine multiple parameters into one unified index. Then healthy baselines of the product are determined based on the unified index. Finally, the method of Relevance Vector Machines is applied to predict the change trend of the unified index and find the failure moment. A case study is presented to prove the validity of our proposed method.
蒋平、邢云燕
自动化技术、自动化技术设备
应用概率多元退化马氏距离相关向量机
pplied probabilityMultivariateDegradationMahalanobis distanceRelevance Vector Machines
蒋平,邢云燕.基于马氏距离和相关向量机的多变量退化预计[EB/OL].(2017-05-18)[2025-08-16].http://www.paper.edu.cn/releasepaper/content/201705-1171.点此复制
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