Maximum average impulse energy ratio deconvolution and its application for periodic fault impulses enhancement of rolling bearing. (August 2022)
- Record Type:
- Journal Article
- Title:
- Maximum average impulse energy ratio deconvolution and its application for periodic fault impulses enhancement of rolling bearing. (August 2022)
- Main Title:
- Maximum average impulse energy ratio deconvolution and its application for periodic fault impulses enhancement of rolling bearing
- Authors:
- Wang, Jinxi
Zhang, Faye
Zhang, Lei
Jiang, Mingshun - Abstract:
- Highlights: The maximum average impulse energy ratio deconvolution method is proposed. The Morlet filter is appointed as the initial filter in the deconvolution process. The fault period is detected with the assistance of the autocorrelation function. The effectiveness is verified by simulation analysis and experimental cases. Abstract: Periodic fault impulses, inevitably occurring along with a localized defect, are crucial for incipient fault diagnosis of rotating machinery. Whereas, they are awfully weak and masked by other interferences in industrial applications. Blind deconvolution methods (BDMs) are diffusely used in enhancing periodic fault impulses submerged in vibration signals. Due to some drawbacks, the applications of traditional BDMs are restricted. Therefore, the maximum average impulse energy ratio deconvolution (MAIERD) method is proposed, where the maximization of a new index called average impulse energy ratio (AIER) is tailored as the objective function. AIER takes the sampling point with the largest amplitude in every fault period as the location of fault impulse. Also, the fault period is detected by the autocorrelation function of the envelope signal. Furthermore, the Morlet wavelet is appointed as the initial filter. The synthesized signals and experimental data collected from two different rolling bearing test rigs are processed for verification. The results show that the proposed MAIERD method is superior in enhancing periodic fault impulses comparedHighlights: The maximum average impulse energy ratio deconvolution method is proposed. The Morlet filter is appointed as the initial filter in the deconvolution process. The fault period is detected with the assistance of the autocorrelation function. The effectiveness is verified by simulation analysis and experimental cases. Abstract: Periodic fault impulses, inevitably occurring along with a localized defect, are crucial for incipient fault diagnosis of rotating machinery. Whereas, they are awfully weak and masked by other interferences in industrial applications. Blind deconvolution methods (BDMs) are diffusely used in enhancing periodic fault impulses submerged in vibration signals. Due to some drawbacks, the applications of traditional BDMs are restricted. Therefore, the maximum average impulse energy ratio deconvolution (MAIERD) method is proposed, where the maximization of a new index called average impulse energy ratio (AIER) is tailored as the objective function. AIER takes the sampling point with the largest amplitude in every fault period as the location of fault impulse. Also, the fault period is detected by the autocorrelation function of the envelope signal. Furthermore, the Morlet wavelet is appointed as the initial filter. The synthesized signals and experimental data collected from two different rolling bearing test rigs are processed for verification. The results show that the proposed MAIERD method is superior in enhancing periodic fault impulses compared with five popular deconvolution methods. … (more)
- Is Part Of:
- Advanced engineering informatics. Volume 53(2022)
- Journal:
- Advanced engineering informatics
- Issue:
- Volume 53(2022)
- Issue Display:
- Volume 53, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 53
- Issue:
- 2022
- Issue Sort Value:
- 2022-0053-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-08
- Subjects:
- Blind deconvolution -- Average impulse energy ratio -- Periodic fault impulses enhancement -- Rolling bearing
Computer-aided engineering -- Periodicals
Engineering -- Data processing -- Periodicals
620.00285 - Journal URLs:
- http://www.sciencedirect.com/science/journal/14740346 ↗
http://books.google.com/books?id=KhFVAAAAMAAJ ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.aei.2022.101721 ↗
- Languages:
- English
- ISSNs:
- 1474-0346
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 0696.851100
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 23316.xml