基于时间迁移模型的旋转机械实时故障诊断

Time transfer model based rotating machine real-time fault diagnosis

提出一种时间迁移模型,以提升旋转机械工况发生变化时的实时故障诊断性能,其由历史数据构成源领域、当前数据构成目标领域。首先,根据变工况规则确定模型的数据领域,并提取其时域特征向量构成五维空间。其次,将源和目标领域通过最大方差投影(MVP)和流形正则化投影(MRP)分别映射至二维子空间,并利用最小均值差异(MMD)准则缩短两者距离。最后,在投影空间中利用BP神经网络和支持向量机(SVM)分类器对源领域建立分类模型,并应用至目标领域,并通过筛选源领域样本以更新诊断模型。齿轮传动系统试验结果表明,时间迁移能够解决工况发生变化时的实时机械故障诊断问题,相比传统迁移成分分析(TCA)模型能提升诊断性能,故为其工程应用提供有价值的技术手段。

A new time transfer model is proposed to enhance the real-time fault diagnosis performance of rotating machine when the working condition change occurs. Here the source domain is composed of historical data and the target domain is composed of current measurement data. Firstly, the data domains of the model are determined according to the varying working condition rules, and their time-domain feature vectors are extracted to construct the five-dimension spaces. Secondly, the source and target domains are mapped into a two-dimension sub-space using the maximum variance projection(MVP) and the manifold regularization projection(MRP), respectively. Meanwhile, the minimum mean difference(MMD) criterion is used to minimize the distance between source domain and target domain in two-dimension space. Finally, in the projection space, the BP neural network and support vector machine(SVM) classifiers are adopted to build the classification models of the source domain, which are applied in target domain. Also, the diagnostic model is updated through selecting the samples in source domains. Experiments on the gear drive-train system were conducted, the experiment results prove that the time transfer model can solve the real-time mechanical fault diagnosis problem when the working condition change occurs. Compared with traditional transfer component analysis(TCA) model, the proposed time transfer model can improve the diagnostic performance, the proposed model provides a valuable technical solution for the engineering application of mechanical fault diagnosis.