Early detection of bearing faults using minimum entropy deconvolution adjusted and zero frequency filter
A method based on minimum entropy deconvolution with convolution adjustment and zero frequency filter is presented for the identification of weak faults in rolling element bearings. Localized fault present in rolling element bearings causes periodic impulses in the bearing vibration signal. The zero...
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| Published in: | Journal of vibration and control Vol. 28; no. 9-10; pp. 1011 - 1024 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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London, England
SAGE Publications
01-05-2022
SAGE PUBLICATIONS, INC |
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| Abstract | A method based on minimum entropy deconvolution with convolution adjustment and zero frequency filter is presented for the identification of weak faults in rolling element bearings. Localized fault present in rolling element bearings causes periodic impulses in the bearing vibration signal. The zero frequency filtering of the bearing vibration signal keeps only the localized disturbances at the impulse locations while attenuating the non-impulsive components of the signal. The effectiveness of zero frequency filtering depends on the strength of impulses present in the measured faulty bearing signal in time domain. In the present work, Minimum entropy deconvolution adjusted is used as a preprocessor to improve the strength of impulses in the measured time-domain bearing signal. The effectiveness of the proposed algorithm is tested with simulated signals for the faulty bearing vibration at different levels of added Gaussian noise. The algorithm is also validated using experimental bearing vibration dataset. Results from the proposed algorithm are compared with the results of the zero frequency filter and local mean subtraction-based technique for rolling element bearings’ fault identification. The proposed algorithm performs better detection in case of a weak fault signal. |
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| AbstractList | A method based on minimum entropy deconvolution with convolution adjustment and zero frequency filter is presented for the identification of weak faults in rolling element bearings. Localized fault present in rolling element bearings causes periodic impulses in the bearing vibration signal. The zero frequency filtering of the bearing vibration signal keeps only the localized disturbances at the impulse locations while attenuating the non-impulsive components of the signal. The effectiveness of zero frequency filtering depends on the strength of impulses present in the measured faulty bearing signal in time domain. In the present work, Minimum entropy deconvolution adjusted is used as a preprocessor to improve the strength of impulses in the measured time-domain bearing signal. The effectiveness of the proposed algorithm is tested with simulated signals for the faulty bearing vibration at different levels of added Gaussian noise. The algorithm is also validated using experimental bearing vibration dataset. Results from the proposed algorithm are compared with the results of the zero frequency filter and local mean subtraction-based technique for rolling element bearings’ fault identification. The proposed algorithm performs better detection in case of a weak fault signal. |
| Author | Kumar, Keshav Singh, Sachin K Shukla, Sumitra |
| Author_xml | – sequence: 1 givenname: Keshav surname: Kumar fullname: Kumar, Keshav organization: Department of Electronics Engineering – sequence: 2 givenname: Sumitra surname: Shukla fullname: Shukla, Sumitra organization: Department of Electronics Engineering – sequence: 3 givenname: Sachin K orcidid: 0000-0002-9754-5415 surname: Singh fullname: Singh, Sachin K email: sachinsingh47@gmail.com organization: Department of Electronics Engineering |
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| Cites_doi | 10.1002/9780470977668 10.1080/00031305.2014.917055 10.1016/j.ymssp.2007.08.006 10.1016/j.asoc.2010.08.011 10.1115/1.1850534 10.1109/79.91217 10.1016/j.ymssp.2015.04.021 10.1016/j.jsv.2015.12.052 10.1006/mssp.2000.1290 10.1016/j.bspc.2019.101762 10.1016/j.ymssp.2011.07.006 10.1016/S0301-679X(99)00077-8 10.1016/0016-7142(78)90005-4 10.1016/j.matpr.2017.02.054 10.1016/j.triboint.2019.106088 10.1016/j.ymssp.2006.12.004 10.1016/j.jsv.2014.11.015 10.1016/j.ymssp.2012.06.010 10.1016/j.measurement.2015.03.041 10.1016/j.jsv.2018.01.022 10.1016/j.ymssp.2016.12.036 10.1016/j.ymssp.2015.03.030 10.1006/mssp.1996.0001 10.1016/j.ymssp.2017.07.037 10.1016/j.ymssp.2015.02.008 10.1016/S0963-8695(01)00044-5 10.1016/j.ymssp.2013.02.016 10.1016/j.measurement.2017.02.033 10.1109/TASL.2008.2004526 10.1016/S0301-679X(02)00063-4 10.1016/j.ymssp.2016.05.036 |
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| Keywords | weak fault detection distant location of vibration sensor Bearing fault zero frequency resonator convolution adjustment minimum entropy deconvolution |
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| SubjectTerms | Algorithms Bearing strength Bearings Deconvolution Entropy Fault detection Frequency filters Impulses Random noise Roller bearings Subtraction Time domain analysis Time measurement Vibration |
| Title | Early detection of bearing faults using minimum entropy deconvolution adjusted and zero frequency filter |
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