Application of Full Vector Deep Learning in Bearing Fault Diagnosis
To handle the numerous and jumbled data from fault monitoring systems,considering information missing with the single channel signal and the complexity and non generality of traditional intelligent diagnostic manual extracting features,a method named full vector deep learning in intelligent fault di...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Transmission
2019-01-01
|
| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2019.01.029 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841548742847627264 |
|---|---|
| author | Chen Chaoyu Chen Lei Zhang Wang Han Jie |
| author_facet | Chen Chaoyu Chen Lei Zhang Wang Han Jie |
| author_sort | Chen Chaoyu |
| collection | DOAJ |
| description | To handle the numerous and jumbled data from fault monitoring systems,considering information missing with the single channel signal and the complexity and non generality of traditional intelligent diagnostic manual extracting features,a method named full vector deep learning in intelligent fault diagnosis of rolling bearing is put forward. Firstly,full vector spectrum is used to fuze the binary channel signal,the main vibration vector data after full vector fusion is acquired,the disadvantage of incomplete single channel vibration signal is overcome. Then,a full-vector deep neural network is built on this basis,combining sparse coding and de-noising coding algorithm,the fault features can be extracted automatically. Finally,the back-propagation algorithm is used to fine-tune the whole network. Experimental results show that the presented method can extract more effective fault features automatically,the classification accuracy and stability of diagnosis are improved,and the complex process of traditional methods is improved. |
| format | Article |
| id | doaj-art-9fe448e2a9b44e719f938df9298bbd67 |
| institution | Kabale University |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2019-01-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-9fe448e2a9b44e719f938df9298bbd672025-01-10T14:02:35ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392019-01-014314414929940406Application of Full Vector Deep Learning in Bearing Fault DiagnosisChen ChaoyuChen LeiZhang WangHan JieTo handle the numerous and jumbled data from fault monitoring systems,considering information missing with the single channel signal and the complexity and non generality of traditional intelligent diagnostic manual extracting features,a method named full vector deep learning in intelligent fault diagnosis of rolling bearing is put forward. Firstly,full vector spectrum is used to fuze the binary channel signal,the main vibration vector data after full vector fusion is acquired,the disadvantage of incomplete single channel vibration signal is overcome. Then,a full-vector deep neural network is built on this basis,combining sparse coding and de-noising coding algorithm,the fault features can be extracted automatically. Finally,the back-propagation algorithm is used to fine-tune the whole network. Experimental results show that the presented method can extract more effective fault features automatically,the classification accuracy and stability of diagnosis are improved,and the complex process of traditional methods is improved.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2019.01.029Intelligent diagnosisDeep learningFull vector spectrumSparse auto-encoder |
| spellingShingle | Chen Chaoyu Chen Lei Zhang Wang Han Jie Application of Full Vector Deep Learning in Bearing Fault Diagnosis Jixie chuandong Intelligent diagnosis Deep learning Full vector spectrum Sparse auto-encoder |
| title | Application of Full Vector Deep Learning in Bearing Fault Diagnosis |
| title_full | Application of Full Vector Deep Learning in Bearing Fault Diagnosis |
| title_fullStr | Application of Full Vector Deep Learning in Bearing Fault Diagnosis |
| title_full_unstemmed | Application of Full Vector Deep Learning in Bearing Fault Diagnosis |
| title_short | Application of Full Vector Deep Learning in Bearing Fault Diagnosis |
| title_sort | application of full vector deep learning in bearing fault diagnosis |
| topic | Intelligent diagnosis Deep learning Full vector spectrum Sparse auto-encoder |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2019.01.029 |
| work_keys_str_mv | AT chenchaoyu applicationoffullvectordeeplearninginbearingfaultdiagnosis AT chenlei applicationoffullvectordeeplearninginbearingfaultdiagnosis AT zhangwang applicationoffullvectordeeplearninginbearingfaultdiagnosis AT hanjie applicationoffullvectordeeplearninginbearingfaultdiagnosis |