Deep Fault Recognizer: An Integrated Model to Denoise and Extract Features for Fault Diagnosis in Rotating Machinery
Fault diagnosis in rotating machinery is significant to avoid serious accidents; thus, an accurate and timely diagnosis method is necessary. With the breakthrough in deep learning algorithm, some intelligent methods, such as deep belief network (DBN) and deep convolution neural network (DCNN), have...
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-12-01
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| Series: | Applied Sciences |
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| Online Access: | http://www.mdpi.com/2076-3417/7/1/41 |
| _version_ | 1818067686019563520 |
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| author | Xiaojie Guo Changqing Shen Liang Chen |
| author_facet | Xiaojie Guo Changqing Shen Liang Chen |
| author_sort | Xiaojie Guo |
| collection | DOAJ |
| description | Fault diagnosis in rotating machinery is significant to avoid serious accidents; thus, an accurate and timely diagnosis method is necessary. With the breakthrough in deep learning algorithm, some intelligent methods, such as deep belief network (DBN) and deep convolution neural network (DCNN), have been developed with satisfactory performances to conduct machinery fault diagnosis. However, only a few of these methods consider properly dealing with noises that exist in practical situations and the denoising methods are in need of extensive professional experiences. Accordingly, rethinking the fault diagnosis method based on deep architectures is essential. Hence, this study proposes an automatic denoising and feature extraction method that inherently considers spatial and temporal correlations. In this study, an integrated deep fault recognizer model based on the stacked denoising autoencoder (SDAE) is applied to both denoise random noises in the raw signals and represent fault features in fault pattern diagnosis for both bearing rolling fault and gearbox fault, and trained in a greedy layer-wise fashion. Finally, the experimental validation demonstrates that the proposed method has better diagnosis accuracy than DBN, particularly in the existing situation of noises with superiority of approximately 7% in fault diagnosis accuracy. |
| first_indexed | 2024-12-10T15:27:37Z |
| format | Article |
| id | doaj.art-3652d30c15e94dd8bbb4da955a927578 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-10T15:27:37Z |
| publishDate | 2016-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-3652d30c15e94dd8bbb4da955a9275782022-12-22T01:43:30ZengMDPI AGApplied Sciences2076-34172016-12-01714110.3390/app7010041app7010041Deep Fault Recognizer: An Integrated Model to Denoise and Extract Features for Fault Diagnosis in Rotating MachineryXiaojie Guo0Changqing Shen1Liang Chen2School of Mechanical and Electric Engineering, Soochow University, Suzhou 215131, ChinaSchool of Mechanical and Electric Engineering, Soochow University, Suzhou 215131, ChinaSchool of Mechanical and Electric Engineering, Soochow University, Suzhou 215131, ChinaFault diagnosis in rotating machinery is significant to avoid serious accidents; thus, an accurate and timely diagnosis method is necessary. With the breakthrough in deep learning algorithm, some intelligent methods, such as deep belief network (DBN) and deep convolution neural network (DCNN), have been developed with satisfactory performances to conduct machinery fault diagnosis. However, only a few of these methods consider properly dealing with noises that exist in practical situations and the denoising methods are in need of extensive professional experiences. Accordingly, rethinking the fault diagnosis method based on deep architectures is essential. Hence, this study proposes an automatic denoising and feature extraction method that inherently considers spatial and temporal correlations. In this study, an integrated deep fault recognizer model based on the stacked denoising autoencoder (SDAE) is applied to both denoise random noises in the raw signals and represent fault features in fault pattern diagnosis for both bearing rolling fault and gearbox fault, and trained in a greedy layer-wise fashion. Finally, the experimental validation demonstrates that the proposed method has better diagnosis accuracy than DBN, particularly in the existing situation of noises with superiority of approximately 7% in fault diagnosis accuracy.http://www.mdpi.com/2076-3417/7/1/41fault diagnosisdeep learningstacked denoising autoencoderfeature extractionintegrated deep fault recognizer |
| spellingShingle | Xiaojie Guo Changqing Shen Liang Chen Deep Fault Recognizer: An Integrated Model to Denoise and Extract Features for Fault Diagnosis in Rotating Machinery Applied Sciences fault diagnosis deep learning stacked denoising autoencoder feature extraction integrated deep fault recognizer |
| title | Deep Fault Recognizer: An Integrated Model to Denoise and Extract Features for Fault Diagnosis in Rotating Machinery |
| title_full | Deep Fault Recognizer: An Integrated Model to Denoise and Extract Features for Fault Diagnosis in Rotating Machinery |
| title_fullStr | Deep Fault Recognizer: An Integrated Model to Denoise and Extract Features for Fault Diagnosis in Rotating Machinery |
| title_full_unstemmed | Deep Fault Recognizer: An Integrated Model to Denoise and Extract Features for Fault Diagnosis in Rotating Machinery |
| title_short | Deep Fault Recognizer: An Integrated Model to Denoise and Extract Features for Fault Diagnosis in Rotating Machinery |
| title_sort | deep fault recognizer an integrated model to denoise and extract features for fault diagnosis in rotating machinery |
| topic | fault diagnosis deep learning stacked denoising autoencoder feature extraction integrated deep fault recognizer |
| url | http://www.mdpi.com/2076-3417/7/1/41 |
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