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KPCA and AE Based Local-Global Feature Extraction Method for Vibration Signals of Rotating Machinery
Mathematical Problems in Engineering ( IF 1.430 ) Pub Date : 2020-06-29 , DOI: 10.1155/2020/5804509
Xiao Hu 1 , Zhihuai Xiao 2 , Dong Liu 3 , Yongjun Tang 4 , O. P. Malik 5 , Xiangchen Xia 1
Affiliation  

Feature extraction plays a key role in fault diagnosis of rotating machinery. Many methods reported in the literature are based on masses of labeled data and need much prior knowledge to select the most discriminating features or establish a complex deep-learning model. To solve the dilemma, a novel feature extraction method based on kernel principal component analysis (KPCA) and an autoencoder (AE), namely, SFS-KPCA-AE, is presented in this paper to automatically extract the most discriminative features from the frequency spectrum of vibration signals. First, fast Fourier transform is calculated on the entire vibration signal to get the frequency spectrum. Next, the spectrum is divided into several segments. Then, local-global feature extraction is performed by applying KPCA to these segments. Finally, an AE is employed to obtain the low-dimensional representations of the high-dimensional global feature. The proposed feature extraction method combined with a classifier achieves fault diagnosis for rotating machinery. A rotor dataset and a bearing dataset are utilized to validate the performance of the proposed method. Experimental results demonstrate that the proposed method achieved satisfactory performance in feature extraction when training samples or motor load changed. By comparing with other methods, the superiority of the proposed SFS-KPCA-AE is verified.

中文翻译:

基于KPCA和AE的旋转机械振动信号局部全局特征提取方法

特征提取在旋转机械故障诊断中起着关键作用。文献中报道的许多方法都是基于大量标记数据,需要很多先验知识才能选择最有区别的功能或建立复杂的深度学习模型。为了解决这一难题,本文提出了一种基于核主成分分析(KPCA)和自动编码器(AE)的新特征提取方法,即SFS-KPCA-AE,可以从频谱中自动提取最具区别性的特征。振动信号。首先,对整个振动信号进行快速傅里叶变换,得到频谱。接下来,将频谱分为几个部分。然后,通过将KPCA应用于这些段来执行局部全局特征提取。最后,利用AE获得高维全局特征的低维表示。提出的特征提取方法与分类器相结合,实现了旋转机械故障诊断。利用转子数据集和轴承数据集来验证所提出方法的性能。实验结果表明,该方法在训练样本或电机负载变化时,在特征提取中均取得了令人满意的性能。通过与其他方法的比较,验证了所提出的SFS-KPCA-AE的优越性。实验结果表明,该方法在训练样本或电机负载变化时,在特征提取中均取得了令人满意的性能。通过与其他方法的比较,验证了所提出的SFS-KPCA-AE的优越性。实验结果表明,该方法在训练样本或电机负载变化时,在特征提取中均取得了令人满意的性能。通过与其他方法的比较,验证了所提出的SFS-KPCA-AE的优越性。
更新日期:2020-06-29
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