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Detection and diagnosis of fault bearing using wavelet packet transform and neural network

Frattura ed integritá strutturale, 2019-07, Vol.13 (49), p.291-301

2019. This article is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1971-8993 ;EISSN: 1971-8993 ;DOI: 10.3221/IGF-ESIS.49.29

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Title:
Detection and diagnosis of fault bearing using wavelet packet transform and neural network
Author: DJABALLAH, Said ; Meftah, Kamel ; Khelil, Khaled ; Tedjini, Mohsein ; Sedira, Lakhdar
Subjects: Artificial neural networks ; Bearing ; Bearings ; Classification ; Classifiers ; Computer simulation ; Conditional maintenance ; Defects ; Diagnosis ; Frequency analysis ; Indicators ; Neural networks ; Preventive maintenance ; Rotating machinery ; The wavelet transform ; Time domain analysis ; Vibration ; Vibration analysis ; Wavelet analysis ; Wavelet transforms
Is Part Of: Frattura ed integritá strutturale, 2019-07, Vol.13 (49), p.291-301
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
Publisher: Cassino: Gruppo Italiano Frattura
Language: English;Italian
Identifier: ISSN: 1971-8993
EISSN: 1971-8993
DOI: 10.3221/IGF-ESIS.49.29
Source: ROAD: Directory of Open Access Scholarly Resources
ProQuest Central
DOAJ Directory of Open Access Journals

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Detection and diagnosis of fault bearing using wavelet packet transform and neural network

2019. This article is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1971-8993 ;EISSN: 1971-8993 ;DOI: 10.3221/IGF-ESIS.49.29

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