웨이블릿변환이 접목된 포락처리를 이용한 저속 회전하는 구름요소베어링 결함 진단Low Speed Rolling Bearing Fault Detection Using AE Signal Analyzed By Envelop Analysis Added DWT
- Other Titles
- Low Speed Rolling Bearing Fault Detection Using AE Signal Analyzed By Envelop Analysis Added DWT
- Authors
- 김병수; 구동식; 김재구; 김원철; 최병근
- Issue Date
- 2009
- Publisher
- 한국마린엔지니어링학회
- Keywords
- Acoustic emission(음향방출); Envelope analysis(포락처리); Discrete wavelet
transform(이산웨이블릿 변환); Peak ratio(피크비); Rolling element bearing(구름
요소베어링)
- Citation
- 한국마린엔지니어링학회지, v.33, no.5, pp 672 - 678
- Pages
- 7
- Indexed
- KCI
- Journal Title
- 한국마린엔지니어링학회지
- Volume
- 33
- Number
- 5
- Start Page
- 672
- End Page
- 678
- URI
- https://scholarworks.gnu.ac.kr/handle/sw.gnu/26795
- ISSN
- 2234-7925
2765-4796
- Abstract
- Acoustic Emission (AE) technique is a non-destructive testing method and
widely used for the early detection of faults in rotating machines in these days, because the sensitivity of AE transducers is higher than normal accelerometers. So it can detect low energy vibration signals. The faults in the rotating machines are generally occurred at bearings and gearboxes which are the principal parts of the machines. It was studied to detect the bearing faults by envelop analysis in several decade years. And the
researches showed that AE had a possibility of the application in condition monitoring system(CMS) using the envelope analysis for the rolling bearing. And peak ratio (PR) was developed for expression of the bearing condition in condition monitoring system using AE. Noise level is needed to reduce to take exact PR value because the PR is calculated from total root mean square (RMS) and the harmonics peak levels of the defect frequencies of the bearing. Therefore, in this paper, the discrete wavelet transform (DWT) was added in the envelope analysis to reduce the noise level in the AE signals. And then, the PR was calculated and compared with general envelope
analysis result and the result of envelope analysis added the DWT. In the experiment result about inner fault of bearing, defect frequency was difficult to find about only envelop analysis. But it’s easy to find defect frequency after wavelet transform. Therefore, Envelop analysis added wavelet transform was useful method for early detection of default in signal process.
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