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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Vydané v:	IEEE access Ročník 5; s. 15066 - 15079
Hlavní autori:	Qi, Yumei, Shen, Changqing, Wang, Dong, Shi, Juanjuan, Jiang, Xingxing, Zhu, Zhongkui
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.01.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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