Back to Search
Start Over
A normalized Hilbert-Huang transform technique for bearing fault detection
- Source :
- Journal of Vibration and Control. 22:2771-2787
- Publication Year :
- 2014
- Publisher :
- SAGE Publications, 2014.
-
Abstract
- Bearings are commonly used in rotary machinery, whereas up to half of machinery malfunctions could be related to bearing defects. Unfortunately reliable fault detection systems still remain a challenging task, especially when bearing defect-related features are nonstationary. A new normalized Hilbert-Huang transform (NHHT) technique is proposed in this paper for vibration-based bearing fault detection. The NHHT for bearing fault detection takes two processes: firstly the vibration signal is denoised to highlight defect-related impulses; and secondly representative features are extracted for bearing fault detection. Vibration signal denoising is carried out by the use of the maximum kurtosis deconvolution filter to reduce impedance effect of transmission path of the measured vibration signal. A novel strategy based on D’Agostino-Person normality is suggested to enhance the distinctive intrinsic mode functions for representative features extraction and formulation for bearing fault detection. The effectiveness of the proposed NHHT technique is verified by a series of experimental tests corresponding to different bearing health conditions, and its robustness in bearing fault detection is examined by the use of data sets from a different experimental setup.
- Subjects :
- 0209 industrial biotechnology
Engineering
Bearing (mechanical)
business.industry
Mechanical Engineering
020208 electrical & electronic engineering
Aerospace Engineering
Pattern recognition
02 engineering and technology
Bearing fault detection
Vibration signal analysis
Hilbert–Huang transform
Fault detection and isolation
law.invention
020901 industrial engineering & automation
Mechanics of Materials
law
Automotive Engineering
0202 electrical engineering, electronic engineering, information engineering
Electronic engineering
General Materials Science
Artificial intelligence
business
Subjects
Details
- ISSN :
- 17412986 and 10775463
- Volume :
- 22
- Database :
- OpenAIRE
- Journal :
- Journal of Vibration and Control
- Accession number :
- edsair.doi...........0f2f324d97c7796d21463c27dc1fe4ad