Stacked Sparse Autoencoder-Based Deep Network for Fault Diagnosis of Rotating Machinery

As a breakthrough in the field of machine fault diagnosis, deep learning has great potential to extract more abstract and discriminative features automatically without much prior knowledge compared with other methods, such as the signal processing and analysis-based methods and machine learning meth...

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Bibliographic Details
Published in:	IEEE access Vol. 5; pp. 15066 - 15079
Main Authors:	Qi, Yumei, Shen, Changqing, Wang, Dong, Shi, Juanjuan, Jiang, Xingxing, Zhu, Zhongkui
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.01.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	autoregressive model Autoregressive models Autoregressive processes Data mining Empirical analysis ensemble empirical mode decomposition Fault diagnosis Feature extraction Learning systems Machine learning Machinery Redundancy Rotating machinery Signal processing Sparse autoencoder Support vector machines
ISSN:	2169-3536, 2169-3536
Online Access:	Get full text
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