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所属单位：能源与动力学院

发表刊物：JOURNAL OF VIBROENGINEERING

关键字：fault diagnosis automatic feature extraction sparse filtering t-SNE

摘要：The main challenge of fault diagnosis is to extract excellent fault feature, but these methods usually depend on the manpower and prior knowledge. It is desirable to automatically extract useful feature from input data in an unsupervised way. Hence, an automatic feature extraction method is presented in this paper. The proposed method first captures fault feature from the raw vibration signal by sparse filtering. Considering that the learned feature is high-dimensional data which cannot achieve visualization, t-distributed stochastic neighbor embedding (t-SNE) is further selected as the dimensionality reduction tool to map the learned feature into a three-dimensional feature vector. Consequently, the effectiveness of the proposed method is verified using gearbox and bearing experimental datas. The classification results show that the hybrid method of sparse filtering and t-SNE can well extract discriminative information from the raw vibration signal and can clearly distinguish different fault types. Through comparison analysis, it is also validated that the proposed method is superior to the other methods.

ISSN号：1392-8716

是否译文：否

发表时间：2017-06-01

合写作者：Wang, Jinrui,Jiang, Xingxing,Cheng, Chun

通讯作者：李舜酩