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Classification of fault location and performance degradation of a roller bearing

机译：滚动轴承的故障定位和性能下降的分类

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Effective fault location classification and especially performance degradation assessment of a roller bearing have been the subject extensive research, which can reduce costs and the nonscheduled down time. In this paper, a new fault diagnosis method based on multiple features, kernel principal component analysis (KPCA) and particle swarm optimization-support vector machine (PSO-SVM) is put forward. First, traditional features of the vibration signals in time-domain and frequency-domain are calculated, and then two types of features referred to as singular values and AR model parameters based on ensemble empirical mode decomposition (EEMD) are introduced. After that, the original feature vectors are mapped into higher dimensional space and the kernel principal components are extracted as new feature vectors, which are used as inputs to PSO-SVM. The experimental results show that the new diagnosis approach proposed in this paper can identify not only the fault locations but also the performance degradation of the roller bearing.
机译：有效的故障位置分类，尤其是滚动轴承的性能下降评估已成为广泛研究的课题，可减少成本并减少计划外的停机时间。提出了一种基于多特征，核主成分分析（KPCA）和粒子群优化支持向量机（PSO-SVM）的故障诊断新方法。首先，计算了时域和频域中振动信号的传统特征，然后基于整体经验模态分解（EEMD），介绍了两种类型的特征，即奇异值和AR模型参数。之后，将原始特征向量映射到更高维的空间中，并提取内核主成分作为新的特征向量，以用作PSO-SVM的输入。实验结果表明，本文提出的新的诊断方法不仅可以识别故障位置，而且可以识别滚动轴承的性能下降。

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