Feature Extraction Based on Adaptive Multiwavelets and LTSA for Rotating Machinery Fault Diagnosis
Feature extraction is a key procedure in the fault diagnosis of rotating machinery. To obtain fault features with lower dimensionality and higher sensitivity, a feature extraction method based on adaptive multiwavelets transform (AMWT) and local tangent space alignment (LTSA) is proposed in this pap...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2019/1201084 |
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| _version_ | 1850213743492857856 |
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| author | Na Lu Guangtao Zhang Zhihuai Xiao Om Parkash Malik |
| author_facet | Na Lu Guangtao Zhang Zhihuai Xiao Om Parkash Malik |
| author_sort | Na Lu |
| collection | DOAJ |
| description | Feature extraction is a key procedure in the fault diagnosis of rotating machinery. To obtain fault features with lower dimensionality and higher sensitivity, a feature extraction method based on adaptive multiwavelets transform (AMWT) and local tangent space alignment (LTSA) is proposed in this paper. AMWT is first used to obtain multiple features from the vibration signals of the machine under test to form a high-dimensional feature set. Then, in order to avoid the adverse effect of the irrelevant features in this high-dimensional feature set on the fault diagnosis result, a detection index (DI) is investigated to evaluate the sensitivity of the features and those with lower sensitivity are removed. After that, LTSA is applied for feature fusion to reduce the redundant features in the high-dimensional feature set. To validate the proposed method, performance of four feature extraction schemes based on (i) wavelet and LTSA, (ii) Geronimo, Hardin, and Massopust (GHM) multiwavelets and LTSA, (iii) AMWT and principal component analysis (PCA), and (iv) AMWT and multidimensional scaling (MDS) is compared with the proposed method. The feature extraction results by these methods are then fed into K-medoids classifier to discriminate the faults. The results show that the proposed method can improve the sensitivity of the extracted features and obtain higher fault recognition rate. |
| format | Article |
| id | doaj-art-81cc83c12e1a4b6b85fbc93e2a879583 |
| institution | OA Journals |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-81cc83c12e1a4b6b85fbc93e2a8795832025-08-20T02:09:05ZengWileyShock and Vibration1070-96221875-92032019-01-01201910.1155/2019/12010841201084Feature Extraction Based on Adaptive Multiwavelets and LTSA for Rotating Machinery Fault DiagnosisNa Lu0Guangtao Zhang1Zhihuai Xiao2Om Parkash Malik3School of Water Conservancy & Environment, Zhengzhou University, Zhengzhou, ChinaRundian Energy Science and Technology Co. Ltd., Zhengzhou 450052, ChinaDepartment of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, ChinaDepartment of Electrical and Computer Engineering, University of Calgary, Calgary, CanadaFeature extraction is a key procedure in the fault diagnosis of rotating machinery. To obtain fault features with lower dimensionality and higher sensitivity, a feature extraction method based on adaptive multiwavelets transform (AMWT) and local tangent space alignment (LTSA) is proposed in this paper. AMWT is first used to obtain multiple features from the vibration signals of the machine under test to form a high-dimensional feature set. Then, in order to avoid the adverse effect of the irrelevant features in this high-dimensional feature set on the fault diagnosis result, a detection index (DI) is investigated to evaluate the sensitivity of the features and those with lower sensitivity are removed. After that, LTSA is applied for feature fusion to reduce the redundant features in the high-dimensional feature set. To validate the proposed method, performance of four feature extraction schemes based on (i) wavelet and LTSA, (ii) Geronimo, Hardin, and Massopust (GHM) multiwavelets and LTSA, (iii) AMWT and principal component analysis (PCA), and (iv) AMWT and multidimensional scaling (MDS) is compared with the proposed method. The feature extraction results by these methods are then fed into K-medoids classifier to discriminate the faults. The results show that the proposed method can improve the sensitivity of the extracted features and obtain higher fault recognition rate.http://dx.doi.org/10.1155/2019/1201084 |
| spellingShingle | Na Lu Guangtao Zhang Zhihuai Xiao Om Parkash Malik Feature Extraction Based on Adaptive Multiwavelets and LTSA for Rotating Machinery Fault Diagnosis Shock and Vibration |
| title | Feature Extraction Based on Adaptive Multiwavelets and LTSA for Rotating Machinery Fault Diagnosis |
| title_full | Feature Extraction Based on Adaptive Multiwavelets and LTSA for Rotating Machinery Fault Diagnosis |
| title_fullStr | Feature Extraction Based on Adaptive Multiwavelets and LTSA for Rotating Machinery Fault Diagnosis |
| title_full_unstemmed | Feature Extraction Based on Adaptive Multiwavelets and LTSA for Rotating Machinery Fault Diagnosis |
| title_short | Feature Extraction Based on Adaptive Multiwavelets and LTSA for Rotating Machinery Fault Diagnosis |
| title_sort | feature extraction based on adaptive multiwavelets and ltsa for rotating machinery fault diagnosis |
| url | http://dx.doi.org/10.1155/2019/1201084 |
| work_keys_str_mv | AT nalu featureextractionbasedonadaptivemultiwaveletsandltsaforrotatingmachineryfaultdiagnosis AT guangtaozhang featureextractionbasedonadaptivemultiwaveletsandltsaforrotatingmachineryfaultdiagnosis AT zhihuaixiao featureextractionbasedonadaptivemultiwaveletsandltsaforrotatingmachineryfaultdiagnosis AT omparkashmalik featureextractionbasedonadaptivemultiwaveletsandltsaforrotatingmachineryfaultdiagnosis |