VMD based trigonometric entropy measure: a simple and effective tool for dynamic degradation monitoring of rolling element bearing
Early identification of rolling element defects is always a topic of interest for researchers and the industry. For early fault identification, a simple and effective dynamic degradation monitoring method using variational mode decomposition (VMD) based trigonometric entropy measure is developed. Fi...
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| Published in | Measurement science & technology Vol. 33; no. 1; p. 14005 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
01.01.2022
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| Online Access | Get full text |
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| Abstract | Early identification of rolling element defects is always a topic of interest for researchers and the industry. For early fault identification, a simple and effective dynamic degradation monitoring method using variational mode decomposition (VMD) based trigonometric entropy measure is developed. First, vibration signals are obtained and are further decomposed using VMD to obtain various frequency modes. Second, a trigonometric entropy measure is developed to monitor the dynamic change occurring in the health of bearing. Third, trigonometric entropy measure of various VMD modes is computed. Fourth, the variance of measure is computed and two modes having the highest variance are selected for principal component analysis (PCA). Thereafter, PCA of selected measures is carried out. Finally, dynamic degradation monitoring is carried out by observing the trend in the principal component having the highest diverse information. The testing of newly developed VMD based trigonometric entropy measure is carried out on the two different types of data set. One is from XJTU-SY Bearing datasets and another is from the Centre for Intelligent Maintenance Systems. The experimental study reveals that the proposed method is capable of raising the alarm about the initiation of defects at a very early stage. Compared to existing indicators such as kurtosis, RMS, and Shannon entropy, the proposed method is superior while carrying out defect degradation monitoring. |
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| AbstractList | Early identification of rolling element defects is always a topic of interest for researchers and the industry. For early fault identification, a simple and effective dynamic degradation monitoring method using variational mode decomposition (VMD) based trigonometric entropy measure is developed. First, vibration signals are obtained and are further decomposed using VMD to obtain various frequency modes. Second, a trigonometric entropy measure is developed to monitor the dynamic change occurring in the health of bearing. Third, trigonometric entropy measure of various VMD modes is computed. Fourth, the variance of measure is computed and two modes having the highest variance are selected for principal component analysis (PCA). Thereafter, PCA of selected measures is carried out. Finally, dynamic degradation monitoring is carried out by observing the trend in the principal component having the highest diverse information. The testing of newly developed VMD based trigonometric entropy measure is carried out on the two different types of data set. One is from XJTU-SY Bearing datasets and another is from the Centre for Intelligent Maintenance Systems. The experimental study reveals that the proposed method is capable of raising the alarm about the initiation of defects at a very early stage. Compared to existing indicators such as kurtosis, RMS, and Shannon entropy, the proposed method is superior while carrying out defect degradation monitoring. |
| Author | Gandhi, C P Kundu, Pradeep Tang, Hesheng Vashishtha, Govind Glowacz, Adam Shukla, Rajendra Kumar Xiang, Jiawei Kumar, Anil |
| Author_xml | – sequence: 1 givenname: Anil orcidid: 0000-0001-6675-1657 surname: Kumar fullname: Kumar, Anil – sequence: 2 givenname: C P orcidid: 0000-0003-1746-5894 surname: Gandhi fullname: Gandhi, C P – sequence: 3 givenname: Govind orcidid: 0000-0002-5160-9647 surname: Vashishtha fullname: Vashishtha, Govind – sequence: 4 givenname: Pradeep orcidid: 0000-0002-8336-5878 surname: Kundu fullname: Kundu, Pradeep – sequence: 5 givenname: Hesheng orcidid: 0000-0001-9810-9415 surname: Tang fullname: Tang, Hesheng – sequence: 6 givenname: Adam orcidid: 0000-0003-0546-7083 surname: Glowacz fullname: Glowacz, Adam – sequence: 7 givenname: Rajendra Kumar surname: Shukla fullname: Shukla, Rajendra Kumar – sequence: 8 givenname: Jiawei orcidid: 0000-0003-4028-985X surname: Xiang fullname: Xiang, Jiawei |
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