Fault Diagnosis for Rolling Bearing under Variable Conditions Based on Image Recognition
Rolling bearing faults often lead to electromechanical system failure due to its high speed and complex working conditions. Recently, a large amount of fault diagnosis studies for rolling bearing based on vibration data has been reported. However, few studies have focused on fault diagnosis for roll...
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2016/1948029 |
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| author | Bo Zhou Yujie Cheng |
| author_facet | Bo Zhou Yujie Cheng |
| author_sort | Bo Zhou |
| collection | DOAJ |
| description | Rolling bearing faults often lead to electromechanical system failure due to its high speed and complex working conditions. Recently, a large amount of fault diagnosis studies for rolling bearing based on vibration data has been reported. However, few studies have focused on fault diagnosis for rolling bearings under variable conditions. This paper proposes a fault diagnosis method based on image recognition for rolling bearings to realize fault classification under variable working conditions. The proposed method includes the following steps. First, the vibration signal data are transformed into a two-dimensional image based on recurrence plot (RP) technique. Next, a popular feature extraction method which has been widely used in the image field, scale invariant feature transform (SIFT), is employed to extract fault features from the two-dimensional RP and subsequently generate a 128-dimensional feature vector. Third, due to the redundancy of the high-dimensional feature, kernel principal component analysis is utilized to reduce the feature dimensionality. Finally, a neural network classifier trained by probabilistic neural network is used to perform fault diagnosis. Verification experiment results demonstrate the effectiveness of the proposed fault diagnosis method for rolling bearings under variable conditions, thereby providing a promising approach to fault diagnosis for rolling bearings. |
| format | Article |
| id | doaj-art-99525e2aaaf8468b8e842fe52b22bf17 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-99525e2aaaf8468b8e842fe52b22bf172025-02-03T06:01:03ZengWileyShock and Vibration1070-96221875-92032016-01-01201610.1155/2016/19480291948029Fault Diagnosis for Rolling Bearing under Variable Conditions Based on Image RecognitionBo Zhou0Yujie Cheng1School of Reliability and Systems Engineering, Beihang University, No. 37, Xueyuan Road, Haidian District, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, No. 37, Xueyuan Road, Haidian District, Beijing 100191, ChinaRolling bearing faults often lead to electromechanical system failure due to its high speed and complex working conditions. Recently, a large amount of fault diagnosis studies for rolling bearing based on vibration data has been reported. However, few studies have focused on fault diagnosis for rolling bearings under variable conditions. This paper proposes a fault diagnosis method based on image recognition for rolling bearings to realize fault classification under variable working conditions. The proposed method includes the following steps. First, the vibration signal data are transformed into a two-dimensional image based on recurrence plot (RP) technique. Next, a popular feature extraction method which has been widely used in the image field, scale invariant feature transform (SIFT), is employed to extract fault features from the two-dimensional RP and subsequently generate a 128-dimensional feature vector. Third, due to the redundancy of the high-dimensional feature, kernel principal component analysis is utilized to reduce the feature dimensionality. Finally, a neural network classifier trained by probabilistic neural network is used to perform fault diagnosis. Verification experiment results demonstrate the effectiveness of the proposed fault diagnosis method for rolling bearings under variable conditions, thereby providing a promising approach to fault diagnosis for rolling bearings.http://dx.doi.org/10.1155/2016/1948029 |
| spellingShingle | Bo Zhou Yujie Cheng Fault Diagnosis for Rolling Bearing under Variable Conditions Based on Image Recognition Shock and Vibration |
| title | Fault Diagnosis for Rolling Bearing under Variable Conditions Based on Image Recognition |
| title_full | Fault Diagnosis for Rolling Bearing under Variable Conditions Based on Image Recognition |
| title_fullStr | Fault Diagnosis for Rolling Bearing under Variable Conditions Based on Image Recognition |
| title_full_unstemmed | Fault Diagnosis for Rolling Bearing under Variable Conditions Based on Image Recognition |
| title_short | Fault Diagnosis for Rolling Bearing under Variable Conditions Based on Image Recognition |
| title_sort | fault diagnosis for rolling bearing under variable conditions based on image recognition |
| url | http://dx.doi.org/10.1155/2016/1948029 |
| work_keys_str_mv | AT bozhou faultdiagnosisforrollingbearingundervariableconditionsbasedonimagerecognition AT yujiecheng faultdiagnosisforrollingbearingundervariableconditionsbasedonimagerecognition |