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: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Piscataway
IEEE
01.01.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2169-3536, 2169-3536 |
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| Abstract | 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 methods with shallow architectures. One of the most important aspects in measuring the extracted features is whether they can explore more information of the inputs and avoid redundancy to be representative. Thus, a stacked sparse autoencoder (SAE)-based machine fault diagnosis method is proposed in this paper. The penalty term of the SAE can help mine essential information and avoid redundancy. To help the constructed diagnosis network further mine more abstract and representative high-level features, the collected non-stationary and transient signals are preprocessed with ensemble empirical mode decomposition and autoregressive (AR) models to obtain AR parameters, which are extracted based on the intrinsic mode functions (IMFs) and regarded as the low-level features for the inputs of the proposed diagnosis network. Only the first four IMFs are considered, because fault information is mainly reflected in high-frequency IMFs. Experiments and comparisons are complemented to validate the superiority of the presented diagnosis network. Results fully demonstrate that the stacked SAE-based diagnosis method can extract more discriminative high-level features and has a better performance in rotating machinery fault diagnosis compared with the traditional machine learning methods with shallow architectures. |
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| AbstractList | 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 methods with shallow architectures. One of the most important aspects in measuring the extracted features is whether they can explore more information of the inputs and avoid redundancy to be representative. Thus, a stacked sparse autoencoder (SAE)-based machine fault diagnosis method is proposed in this paper. The penalty term of the SAE can help mine essential information and avoid redundancy. To help the constructed diagnosis network further mine more abstract and representative high-level features, the collected non-stationary and transient signals are preprocessed with ensemble empirical mode decomposition and autoregressive (AR) models to obtain AR parameters, which are extracted based on the intrinsic mode functions (IMFs) and regarded as the low-level features for the inputs of the proposed diagnosis network. Only the first four IMFs are considered, because fault information is mainly reflected in high-frequency IMFs. Experiments and comparisons are complemented to validate the superiority of the presented diagnosis network. Results fully demonstrate that the stacked SAE-based diagnosis method can extract more discriminative high-level features and has a better performance in rotating machinery fault diagnosis compared with the traditional machine learning methods with shallow architectures. |
| Author | Juanjuan Shi Yumei Qi Zhongkui Zhu Changqing Shen Dong Wang Xingxing Jiang |
| Author_xml | – sequence: 1 givenname: Yumei surname: Qi fullname: Qi, Yumei – sequence: 2 givenname: Changqing orcidid: 0000-0002-4792-1865 surname: Shen fullname: Shen, Changqing – sequence: 3 givenname: Dong surname: Wang fullname: Wang, Dong – sequence: 4 givenname: Juanjuan surname: Shi fullname: Shi, Juanjuan – sequence: 5 givenname: Xingxing surname: Jiang fullname: Jiang, Xingxing – sequence: 6 givenname: Zhongkui surname: Zhu fullname: Zhu, Zhongkui |
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| References | chen (ref11) 2017; 32 ref12 ref15 ref30 ref10 ref2 ref1 ref17 ref16 ref19 ref18 van den oord (ref14) 2013 ref24 yan (ref3) 2014; 96 ref23 ref26 ref25 ref20 ref22 ref21 liu (ref31) 2016; 120 ref28 ref27 hannun (ref13) 2014 ref29 ref8 ref7 ref9 ref4 ref6 ref5 |
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| SubjectTerms | 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 |
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| Title | Stacked Sparse Autoencoder-Based Deep Network for Fault Diagnosis of Rotating Machinery |
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