Research on Degradation State Recognition of Planetary Gear Based on Multiscale Information Dimension of SSD and CNN
Planetary gear is the key part of the transmission system for large complex electromechanical equipment, and in general, a series of degradation states are undergone and evolved into a local fatal fault in its full life cycle. So it is of great significance to recognize the degradation state of plan...
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Online Access: | http://dx.doi.org/10.1155/2019/8716979 |
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doaj-20cba3f5b5724e44b9f71d313e4b6fb12020-11-25T02:37:03ZengHindawi-WileyComplexity1076-27871099-05262019-01-01201910.1155/2019/87169798716979Research on Degradation State Recognition of Planetary Gear Based on Multiscale Information Dimension of SSD and CNNXihui Chen0Liping Peng1Gang Cheng2Chengming Luo3College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, ChinaCollege of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, ChinaSchool of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaCollege of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, ChinaPlanetary gear is the key part of the transmission system for large complex electromechanical equipment, and in general, a series of degradation states are undergone and evolved into a local fatal fault in its full life cycle. So it is of great significance to recognize the degradation state of planetary gear for the purpose of maintenance repair, predicting development trend, and avoiding sudden fault. This paper proposed a degradation state recognition method of planetary gear based on multiscale information dimension of singular spectrum decomposition (SSD) and convolutional neural network (CNN). SSD can automatically realize the embedding dimension selection and component grouping segmentation, and the original vibration signal being nonlinear and nonstationary can be decomposed into a series of singular spectrum decomposition components (SSDCs), adaptively. Then, the multiscale information dimension which combines multiscale analysis and fractal information dimension is proposed for quantifying and extracting the feature information contained in each SSDC. Finally, CNN is used to achieve the effective recognition of the degradation state of planetary gear. The experimental results show that the proposed method can accurately recognize the degradation state of planetary gear, and the overall recognition rate is up to 97.2%, of which the recognition rate of normal planetary gear reaches 100%.http://dx.doi.org/10.1155/2019/8716979 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xihui Chen Liping Peng Gang Cheng Chengming Luo |
spellingShingle |
Xihui Chen Liping Peng Gang Cheng Chengming Luo Research on Degradation State Recognition of Planetary Gear Based on Multiscale Information Dimension of SSD and CNN Complexity |
author_facet |
Xihui Chen Liping Peng Gang Cheng Chengming Luo |
author_sort |
Xihui Chen |
title |
Research on Degradation State Recognition of Planetary Gear Based on Multiscale Information Dimension of SSD and CNN |
title_short |
Research on Degradation State Recognition of Planetary Gear Based on Multiscale Information Dimension of SSD and CNN |
title_full |
Research on Degradation State Recognition of Planetary Gear Based on Multiscale Information Dimension of SSD and CNN |
title_fullStr |
Research on Degradation State Recognition of Planetary Gear Based on Multiscale Information Dimension of SSD and CNN |
title_full_unstemmed |
Research on Degradation State Recognition of Planetary Gear Based on Multiscale Information Dimension of SSD and CNN |
title_sort |
research on degradation state recognition of planetary gear based on multiscale information dimension of ssd and cnn |
publisher |
Hindawi-Wiley |
series |
Complexity |
issn |
1076-2787 1099-0526 |
publishDate |
2019-01-01 |
description |
Planetary gear is the key part of the transmission system for large complex electromechanical equipment, and in general, a series of degradation states are undergone and evolved into a local fatal fault in its full life cycle. So it is of great significance to recognize the degradation state of planetary gear for the purpose of maintenance repair, predicting development trend, and avoiding sudden fault. This paper proposed a degradation state recognition method of planetary gear based on multiscale information dimension of singular spectrum decomposition (SSD) and convolutional neural network (CNN). SSD can automatically realize the embedding dimension selection and component grouping segmentation, and the original vibration signal being nonlinear and nonstationary can be decomposed into a series of singular spectrum decomposition components (SSDCs), adaptively. Then, the multiscale information dimension which combines multiscale analysis and fractal information dimension is proposed for quantifying and extracting the feature information contained in each SSDC. Finally, CNN is used to achieve the effective recognition of the degradation state of planetary gear. The experimental results show that the proposed method can accurately recognize the degradation state of planetary gear, and the overall recognition rate is up to 97.2%, of which the recognition rate of normal planetary gear reaches 100%. |
url |
http://dx.doi.org/10.1155/2019/8716979 |
work_keys_str_mv |
AT xihuichen researchondegradationstaterecognitionofplanetarygearbasedonmultiscaleinformationdimensionofssdandcnn AT lipingpeng researchondegradationstaterecognitionofplanetarygearbasedonmultiscaleinformationdimensionofssdandcnn AT gangcheng researchondegradationstaterecognitionofplanetarygearbasedonmultiscaleinformationdimensionofssdandcnn AT chengmingluo researchondegradationstaterecognitionofplanetarygearbasedonmultiscaleinformationdimensionofssdandcnn |
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1724796959281119232 |