A New Time-frequency Domain Feature Extraction Method for Rolling Bearing Fault Diagnosis
For fault diagnosis of rolling bearings,a new feature extraction strategy based on short time Fourier transform（ STFT） and bag of wordss（ BOW） is proposed. Based on the generate mechanism of bearing fault,the different bearing vibration signals have relevant energy distribution. But in the factory,s...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Transmission
2016-01-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2016.07.028 |
| _version_ | 1826882043029487616 |
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| author | Chen Junjie Wang Xiaofeng Liu Fei Zhou Wenjing |
| author_facet | Chen Junjie Wang Xiaofeng Liu Fei Zhou Wenjing |
| author_sort | Chen Junjie |
| collection | DOAJ |
| description | For fault diagnosis of rolling bearings,a new feature extraction strategy based on short time Fourier transform（ STFT） and bag of wordss（ BOW） is proposed. Based on the generate mechanism of bearing fault,the different bearing vibration signals have relevant energy distribution. But in the factory,some factors like signal interference or environment noise will destroy the energy distribution. When using BOW,it regards the distribution of energy in frequency domain each time frame as a word,so segments of signal will be documents which are made up of many words. It shows the energy distribution directly in data perspective. Then,with the new features and SVM classifier,the results of fault diagnosis can be known. At last,effectiveness of the proposed method is verified,vibration from SQI- MFS platform and CWRU platform are analyzed. The results in experiments shows that this method is better than RMS and WE&WEE. So the new feature can be used in fault diagnosis area. |
| first_indexed | 2024-03-13T09:22:42Z |
| format | Article |
| id | doaj.art-e916b28765dc4df4b551a433cf7049aa |
| institution | Directory Open Access Journal |
| issn | 1004-2539 |
| language | zho |
| last_indexed | 2025-02-17T03:00:28Z |
| publishDate | 2016-01-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj.art-e916b28765dc4df4b551a433cf7049aa2025-01-10T14:16:53ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392016-01-014012613129925222A New Time-frequency Domain Feature Extraction Method for Rolling Bearing Fault DiagnosisChen JunjieWang XiaofengLiu FeiZhou WenjingFor fault diagnosis of rolling bearings,a new feature extraction strategy based on short time Fourier transform（ STFT） and bag of wordss（ BOW） is proposed. Based on the generate mechanism of bearing fault,the different bearing vibration signals have relevant energy distribution. But in the factory,some factors like signal interference or environment noise will destroy the energy distribution. When using BOW,it regards the distribution of energy in frequency domain each time frame as a word,so segments of signal will be documents which are made up of many words. It shows the energy distribution directly in data perspective. Then,with the new features and SVM classifier,the results of fault diagnosis can be known. At last,effectiveness of the proposed method is verified,vibration from SQI- MFS platform and CWRU platform are analyzed. The results in experiments shows that this method is better than RMS and WE&WEE. So the new feature can be used in fault diagnosis area.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2016.07.028Fault diagnosisTime-frequency domain featureSTFTBOWSVM |
| spellingShingle | Chen Junjie Wang Xiaofeng Liu Fei Zhou Wenjing A New Time-frequency Domain Feature Extraction Method for Rolling Bearing Fault Diagnosis Jixie chuandong Fault diagnosis Time-frequency domain feature STFT BOW SVM |
| title | A New Time-frequency Domain Feature Extraction Method for Rolling Bearing Fault Diagnosis |
| title_full | A New Time-frequency Domain Feature Extraction Method for Rolling Bearing Fault Diagnosis |
| title_fullStr | A New Time-frequency Domain Feature Extraction Method for Rolling Bearing Fault Diagnosis |
| title_full_unstemmed | A New Time-frequency Domain Feature Extraction Method for Rolling Bearing Fault Diagnosis |
| title_short | A New Time-frequency Domain Feature Extraction Method for Rolling Bearing Fault Diagnosis |
| title_sort | new time frequency domain feature extraction method for rolling bearing fault diagnosis |
| topic | Fault diagnosis Time-frequency domain feature STFT BOW SVM |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2016.07.028 |
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