Detection and diagnosis of fault bearing using wavelet packet transform and neural network
Bearings, considered crucial components in rotating machinery, are widely used in the industry. Bearing status monitoring has become an essential step in the deployment of preventive maintenance policy. This work is part of the diagnosis and classification of bearing defects by vibration analysis of...
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| Format: | Article |
| Language: | English |
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Gruppo Italiano Frattura
2019-07-01
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| Series: | Fracture and Structural Integrity |
| Subjects: | |
| Online Access: | https://www.fracturae.com/index.php/fis/article/view/2399/2546 |
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| _version_ | 1832568706254766080 |
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| author | Djaballah Said Meftah Kamel Khelil Khaled Tedjini Mohsein Sedira Lakhdar |
| author_facet | Djaballah Said Meftah Kamel Khelil Khaled Tedjini Mohsein Sedira Lakhdar |
| author_sort | Djaballah Said |
| collection | DOAJ |
| description | Bearings, considered crucial components in rotating machinery, are widely used in the industry. Bearing status monitoring has become an essential step in the deployment of preventive maintenance policy. This work is part of the diagnosis and classification of bearing defects by vibration analysis of signals from defective bearings using time domain and frequency analysis and wavelet packet transformations (Wavelet Packet Transform WPT) with Artificial Neural Networks (ANN). WPT is used for extracting defect indicators to train the neural classifier. The main goal is the determination of the wavelet generating the most representative indicators of the state of the bearings for better detection and classification of defects. Using the WPT-based neural classifier, the obtained simulation results showed that the db6 wavelet with level 3 decomposition is best suited for diagnosing and classifying bearing defects. |
| format | Article |
| id | doaj-art-58c0d80ed2054ddf8b134939622cd6f4 |
| institution | Kabale University |
| issn | 1971-8993 |
| language | English |
| publishDate | 2019-07-01 |
| publisher | Gruppo Italiano Frattura |
| record_format | Article |
| series | Fracture and Structural Integrity |
| spelling | doaj-art-58c0d80ed2054ddf8b134939622cd6f42025-02-03T00:44:58ZengGruppo Italiano FratturaFracture and Structural Integrity1971-89932019-07-01134929130110.3221/IGF-ESIS.49.2910.3221/IGF-ESIS.49.29Detection and diagnosis of fault bearing using wavelet packet transform and neural networkDjaballah SaidMeftah KamelKhelil KhaledTedjini MohseinSedira LakhdarBearings, considered crucial components in rotating machinery, are widely used in the industry. Bearing status monitoring has become an essential step in the deployment of preventive maintenance policy. This work is part of the diagnosis and classification of bearing defects by vibration analysis of signals from defective bearings using time domain and frequency analysis and wavelet packet transformations (Wavelet Packet Transform WPT) with Artificial Neural Networks (ANN). WPT is used for extracting defect indicators to train the neural classifier. The main goal is the determination of the wavelet generating the most representative indicators of the state of the bearings for better detection and classification of defects. Using the WPT-based neural classifier, the obtained simulation results showed that the db6 wavelet with level 3 decomposition is best suited for diagnosing and classifying bearing defects.https://www.fracturae.com/index.php/fis/article/view/2399/2546Conditional maintenanceBearingThe wavelet transformNeural networks. |
| spellingShingle | Djaballah Said Meftah Kamel Khelil Khaled Tedjini Mohsein Sedira Lakhdar Detection and diagnosis of fault bearing using wavelet packet transform and neural network Fracture and Structural Integrity Conditional maintenance Bearing The wavelet transform Neural networks. |
| title | Detection and diagnosis of fault bearing using wavelet packet transform and neural network |
| title_full | Detection and diagnosis of fault bearing using wavelet packet transform and neural network |
| title_fullStr | Detection and diagnosis of fault bearing using wavelet packet transform and neural network |
| title_full_unstemmed | Detection and diagnosis of fault bearing using wavelet packet transform and neural network |
| title_short | Detection and diagnosis of fault bearing using wavelet packet transform and neural network |
| title_sort | detection and diagnosis of fault bearing using wavelet packet transform and neural network |
| topic | Conditional maintenance Bearing The wavelet transform Neural networks. |
| url | https://www.fracturae.com/index.php/fis/article/view/2399/2546 |
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