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所属单位：能源与动力学院

发表刊物：2017 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-HARBIN)

关键字：fault diagnosis stacked autoencoder softmax regression visualization

摘要：Intelligent diagnosis method has been a hot topic in the prognostics and fault diagnosis of rotating machinery. A deep neural network (DNN) method for gear fault diagnosis based on stacked autoencode (SAE) and softmax regression is presented in this work. SAE is first utilized to extract features from the frequency spectra of vibration signal. And then the learned features are used to train softmax regression classifier for identifying gear faults. The diagnosis results validate that the method is feasible and is able to acquire superior classification performance. With the increasing layers of DNN, the learned features of each hidden layer are becoming more and more robust for classification. In this paper, a novel view on the visualization of the learned features is displayed to evaluate the DNN-based fault diagnosis method.

ISSN号：2166-5656

是否译文：否

发表时间：2017-01-01

合写作者：王金瑞,江星星,辛玉

通讯作者：李舜酩