This paper proposes a hybrid intelligent method for multi-fault detection of rotating machinery, in which three methods, i.e. including the redundant second generation wavelet package transform (RSGWPT), the kernel principal component analysis (KPCA) and the twin support vector machine (TWSVM), are combined. Firstly, RSGWPT is used to extract feature vectors from representative statistical characteristics in the decomposition frequency band, and then the KPCA in the feature space is performed to reduce the dimension of features and to extract the dominant features for the following classification. Finally, a novel support vector machine, called twin support vector machine is used to construct a multi-class classifier. Inputting superior features to this classifier, the condition of the monitored machine component can be determined. Experimental results demonstrate that the proposed hybrid method is effective for multi-fault detection of rotating machinery. The TWSVM is also indicated that has better classification performance and faster convergence speed than the normal SVM.

中文翻译：

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基于RSGWPT，KPCA和Twin SVM的旋转机械混合智能多故障检测方法。

提出了一种旋转机械多故障检测的混合智能方法，其中包括冗余第二代小波包变换（RSGWPT），核主成分分析（KPCA）和双支持向量机（TWSVM）三种方法。 ），组合在一起。首先，使用RSGWPT从分解频带中的代表性统计特征中提取特征向量，然后执行特征空间中的KPCA来减少特征的维数并提取主要特征，以进行以下分类。最后，一种新型的支持向量机，称为孪生支持向量机，被用来构造一个多分类器。将高级功能输入该分类器，即可确定受监视机器组件的状况。实验结果表明，所提出的混合方法对于旋转机械的多故障检测是有效的。还表明，TWSVM具有比普通SVM更好的分类性能和更快的收敛速度。