Vibration Analysis of Shaft Misalignment Using Machine Learning Approach under Variable Load Conditions
The Industry 4.0 revolution is insisting strongly for use of machine learning-based processes and condition monitoring. In this paper, emphasis is given on machine learning-based approach for condition monitoring of shaft misalignment. This work highlights combined approach of artificial neural netw...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2020/1650270 |
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| author | A. M. Umbrajkaar A. Krishnamoorthy R. B. Dhumale |
| author_facet | A. M. Umbrajkaar A. Krishnamoorthy R. B. Dhumale |
| author_sort | A. M. Umbrajkaar |
| collection | DOAJ |
| description | The Industry 4.0 revolution is insisting strongly for use of machine learning-based processes and condition monitoring. In this paper, emphasis is given on machine learning-based approach for condition monitoring of shaft misalignment. This work highlights combined approach of artificial neural network and support vector machine for identification and measure of shaft misalignment. The measure of misalignment requires more features to be extracted under variable load conditions. Hence, primary objective is to measure misalignment with a minimum number of extracted features. This is achieved through normalization of vibration signal. An experimental setup is prepared to collect the required vibration signals. The normalized time domain nonstationary signals are given to discrete wavelet transform for features extraction. The extracted features such as detailed coefficient is considered for feature selection viz. Skewness, Kurtosis, Max, Min, Root mean square, and Entropy. The ReliefF algorithm is used to decide best feature on rank basis. The ratio of maximum energy to Shannon entropy is used in wavelet selection. The best feature is used to train machine learning algorithm. The rank-based feature selection has improved classification accuracy of support vector machine. The result obtained with the combined approach are discussed for different misalignment conditions. |
| format | Article |
| id | doaj-art-66e718d79ffe4955b98ee3527d9ecd67 |
| institution | OA Journals |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-66e718d79ffe4955b98ee3527d9ecd672025-08-20T02:23:43ZengWileyShock and Vibration1070-96221875-92032020-01-01202010.1155/2020/16502701650270Vibration Analysis of Shaft Misalignment Using Machine Learning Approach under Variable Load ConditionsA. M. Umbrajkaar0A. Krishnamoorthy1R. B. Dhumale2Department of Mechanical Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamilnadu, IndiaDepartment of Mechanical Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamilnadu, IndiaDepartment of Electronics and Telecommunication, Sinhgad College of Engineering, Pune, Maharashtra, IndiaThe Industry 4.0 revolution is insisting strongly for use of machine learning-based processes and condition monitoring. In this paper, emphasis is given on machine learning-based approach for condition monitoring of shaft misalignment. This work highlights combined approach of artificial neural network and support vector machine for identification and measure of shaft misalignment. The measure of misalignment requires more features to be extracted under variable load conditions. Hence, primary objective is to measure misalignment with a minimum number of extracted features. This is achieved through normalization of vibration signal. An experimental setup is prepared to collect the required vibration signals. The normalized time domain nonstationary signals are given to discrete wavelet transform for features extraction. The extracted features such as detailed coefficient is considered for feature selection viz. Skewness, Kurtosis, Max, Min, Root mean square, and Entropy. The ReliefF algorithm is used to decide best feature on rank basis. The ratio of maximum energy to Shannon entropy is used in wavelet selection. The best feature is used to train machine learning algorithm. The rank-based feature selection has improved classification accuracy of support vector machine. The result obtained with the combined approach are discussed for different misalignment conditions.http://dx.doi.org/10.1155/2020/1650270 |
| spellingShingle | A. M. Umbrajkaar A. Krishnamoorthy R. B. Dhumale Vibration Analysis of Shaft Misalignment Using Machine Learning Approach under Variable Load Conditions Shock and Vibration |
| title | Vibration Analysis of Shaft Misalignment Using Machine Learning Approach under Variable Load Conditions |
| title_full | Vibration Analysis of Shaft Misalignment Using Machine Learning Approach under Variable Load Conditions |
| title_fullStr | Vibration Analysis of Shaft Misalignment Using Machine Learning Approach under Variable Load Conditions |
| title_full_unstemmed | Vibration Analysis of Shaft Misalignment Using Machine Learning Approach under Variable Load Conditions |
| title_short | Vibration Analysis of Shaft Misalignment Using Machine Learning Approach under Variable Load Conditions |
| title_sort | vibration analysis of shaft misalignment using machine learning approach under variable load conditions |
| url | http://dx.doi.org/10.1155/2020/1650270 |
| work_keys_str_mv | AT amumbrajkaar vibrationanalysisofshaftmisalignmentusingmachinelearningapproachundervariableloadconditions AT akrishnamoorthy vibrationanalysisofshaftmisalignmentusingmachinelearningapproachundervariableloadconditions AT rbdhumale vibrationanalysisofshaftmisalignmentusingmachinelearningapproachundervariableloadconditions |