谱质心迁移在变工况轴承故障诊断的应用

Application of spectral centroid transfer inbearing fault diagnosis under varying working conditions

轴承故障诊断普遍存在需建立不同模型以适应变工况的问题,故提出一种谱质心迁移学习模型,通过将源工况领域迁移至目标工况领域减少后者的建模代价,并增强模型通用性。首先计算两工况领域间频谱相似度(FSSM)并排序选择近距离源工况领域为初始训练集。其次在迭代过程中剔除与训练集谱质心均值距离较远的样本,并加入同数量目标工况领域无标签样本,直至两者谱质心均值距离一致,模型故障类别取决于支持向量机(SVM)和逻辑回归(LR)基分类器的输出。Spectra Quest齿轮传动系统试验结果表明,转速负载发生变化时,该模型诊断性能优于非迁移模型,且能够根据替换样本数、精度、频谱相似度、耗时等指标评估源工况领域质量,因此具有解决变工况轴承故障诊断问题的潜在价值。

There is a limitation existing in classical bearing fault diagnosis thatit is required to build different target models to fitvarying working conditions. This paper proposes a spectral centroid transfer learning model, which transfers the source working condition domainto target working condition domain;the modeling cost for target working condition domainis reduced and the universality of bearing fault diagnosis modelis enhanced.Firstly, the frequency spectrum similarity measure (FSSM) value between the two working conditions domains is calculated and the source working condition domain with near distance is sorted and selected as the initial training set. Then, during iteration process, the samples whose spectral centroid meandistances are relatively far from that of the training set are removed, and the same quantity of label less samples from target working condition domain are added to training set. The iteration finishes when the spectral centroidme and istances of both the working conditions domainsare equal. Here the fault categories are determined by the outputs of two sub-classifiers:The support vector machine (SVM) and the logistic regression (LR)based sub-classifiers. The experiment results on Spectra Quest geared drive train show that the diagnostic performance of the proposed model is significantly better than that of nontransfer model when the rotation speed or load changes. Meanwhile, some indexes, including the number of replaced samples, the diagnostic accuracy, the FSSM index and the time consumption can be utilized to evaluate the quality of the source working condition domain.Thus,the proposed model possesses a potential valuein solving bearing fault diagnosis issue under varying working conditions.