An Automated method for the analysis of bearing vibration based on spectrogram pattern matching
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| Natura: | Artículo científico |
| Lingua: | en |
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Universidad Nacional Autónoma de México
2019
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| _version_ | 1876470219933745152 |
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| author | P. Arun |
| author_facet | P. Arun |
| contents | An Automated method for the analysis of bearing vibration based on spectrogram pattern matching P. Arun S. Abraham Lincon N. Prabhakaran Ingeniería vibration spectrogram Bearing fault pattern matching structural similarity index metric As a mean for non-intrusive inspection of bearing systems, the scope of predicting their condition from the acoustic vibrations liberated during their operation, utilizing signal processing methods, has been of extensive research, over decades. Vibration being highly non-stationary, time domain as well as spectral features cannot characterize its behavior. Even though spectrogram is a time-frequency domain feature extraction technique, its interpretation is tedious and perhaps, subjective. In the proposed method, the spectrogram images of the normal vibration data is compared with that of the contextual vibration, using Structural Similarity Index Metric (SSIM). It is hypothesized that the pattern similarity between the contextual spectrogram and baseline is low when the bearing is faulty. The SSIM between the spectrogram image of normal bearing vibration data and the baseline is different from those between the baseline and vibration data corresponding to Inner Race Failure (IRF), Roller Element Defect (RED) and Outer Race Failure (ORF). Via the proposed method of spectrogram pattern matching based on SSIM, the subjectivity in the comparative interpretation of spectrogram is eliminated fully. The SSIM corresponding to the vibrations acquired from normal and faulty bearings differ with a P value of 4.43693xl0-16. The technique can distinguish defective bearings with, 95.74% sensitivity, 96% accuracy and 100% specificity, without dismantling or open intervention. 2019 artículo científico 1665-6423 https://www.redalyc.org/articulo.oa?id=47471656004 https://www.redalyc.org/journal/474/47471656004/ https://www.redalyc.org/journal/474/47471656004/html/ https://www.redalyc.org/journal/474/47471656004/47471656004.epub https://www.redalyc.org/journal/474/47471656004/movil 10.22201/icat.16656423.2019.17.2.805 en http://www.redalyc.org/revista.oa?id=474 Journal of Applied Research and Technology application/pdf Universidad Nacional Autónoma de México Journal of Applied Research and Technology (México) Num.2 Vol.17 |
| format | Artículo científico |
| id | redalyc_47471656004 |
| institution | Redalyc |
| language | en |
| publishDate | 2019 |
| publisher | Universidad Nacional Autónoma de México |
| spellingShingle | An Automated method for the analysis of bearing vibration based on spectrogram pattern matching P. Arun Ingeniería vibration spectrogram Bearing fault pattern matching structural similarity index metric An Automated method for the analysis of bearing vibration based on spectrogram pattern matching P. Arun S. Abraham Lincon N. Prabhakaran Ingeniería vibration spectrogram Bearing fault pattern matching structural similarity index metric As a mean for non-intrusive inspection of bearing systems, the scope of predicting their condition from the acoustic vibrations liberated during their operation, utilizing signal processing methods, has been of extensive research, over decades. Vibration being highly non-stationary, time domain as well as spectral features cannot characterize its behavior. Even though spectrogram is a time-frequency domain feature extraction technique, its interpretation is tedious and perhaps, subjective. In the proposed method, the spectrogram images of the normal vibration data is compared with that of the contextual vibration, using Structural Similarity Index Metric (SSIM). It is hypothesized that the pattern similarity between the contextual spectrogram and baseline is low when the bearing is faulty. The SSIM between the spectrogram image of normal bearing vibration data and the baseline is different from those between the baseline and vibration data corresponding to Inner Race Failure (IRF), Roller Element Defect (RED) and Outer Race Failure (ORF). Via the proposed method of spectrogram pattern matching based on SSIM, the subjectivity in the comparative interpretation of spectrogram is eliminated fully. The SSIM corresponding to the vibrations acquired from normal and faulty bearings differ with a P value of 4.43693xl0-16. The technique can distinguish defective bearings with, 95.74% sensitivity, 96% accuracy and 100% specificity, without dismantling or open intervention. 2019 artículo científico 1665-6423 https://www.redalyc.org/articulo.oa?id=47471656004 https://www.redalyc.org/journal/474/47471656004/ https://www.redalyc.org/journal/474/47471656004/html/ https://www.redalyc.org/journal/474/47471656004/47471656004.epub https://www.redalyc.org/journal/474/47471656004/movil 10.22201/icat.16656423.2019.17.2.805 en http://www.redalyc.org/revista.oa?id=474 Journal of Applied Research and Technology application/pdf Universidad Nacional Autónoma de México Journal of Applied Research and Technology (México) Num.2 Vol.17 |
| title | An Automated method for the analysis of bearing vibration based on spectrogram pattern matching |
| topic | Ingeniería vibration spectrogram Bearing fault pattern matching structural similarity index metric |
| url | https://www.redalyc.org/articulo.oa?id=47471656004 https://www.redalyc.org/journal/474/47471656004/ https://www.redalyc.org/journal/474/47471656004/html/ https://www.redalyc.org/journal/474/47471656004/47471656004.epub https://www.redalyc.org/journal/474/47471656004/movil |