Bearing Fault Detection Using DWT and CNN
DOI:
https://doi.org/10.32628/CSEIT2174116Keywords:
DWT, CNN classifier, image processing, Bearing, MWAAbstract
Bearing is a key component of satellite inertia actuators such as moment wheel assemblies (MWAs) and control moment gyros (CMGs), and its operating state is directly related to the performance and service life of satellites. However, because of the complexity of the vibration frequency components of satellite bearing assemblies and the small loading, normal running bearings normally present similar fault characteristics in long-term ground life experiments, which makes it difficult to judge the bearing fault status. There are various methods introduced for condition monitoring such as vibration analysis, temperature analysis, wear and debris analysis, image processing etc. Among this image analysis is found to be the most effective method for detection of machine faults. This paper proposes an automatic fault diagnosis method for bearings based on a DWT and CNN.
References
- Pravesh Durkhure, Akhilesh Lodwal INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY Fault Diagnosis of Ball Bearing using Time Domain Analysis and Fast Fourier Transformation, Indore (2014)
- Lakshmi Pratyusha, ShanmukhaPriya, VPS Naidu, Bearing Health Condition Monitoring: Time Domain Analysis International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering December (2014).
- Yogita K Chaudhari Vibration Analysis for Bearing Fault Detection in Electrical Motors, ICNSC-14, (2014).
- P. G. Kulkarni, A. D. Sahasrabudhe Application of Wavelet Transform for Fault Diagnosis of Rolling Element Bearings (2013).
- Milind Natu, Bearing Fault Analysis Using Frequency Analysis and Wavelet Analysis, International Journal of Innovation, Management and Technology, Vol. 4, No. 1, February (2013).
- BogdanBetea, LiviuTomesc, and Petru Dobra Automatic Control Department Technical University of Romania, Bearing Defects Diagnosis Based on Gaussian Filtering Shock Detection and Power Spectrum of Envelope (2013).
- Bogdanbetea, mircea-cristiangherman, monicaborda, petrudobraBearing defects signals demodulation using shock filters (2012).
- D. H. Pandya, S. H. Upadhyay, S. P. Harsha., ANN based fault diagnosis of rolling element bearing using time-frequency domain feature International Journal of Engg Science and Technology (IJEST) Vol. 4 No.06 June (2012).
- Xiang-jun Chen, Zhan-fengGao (2011) Data processing based on wavelet analysis in structure health monitoring system of Computers, vol. 6 no.12, pp. 2686-26
- J. Chebil, G. Noel et al (2009) Wavelet decomposition for the detection and diagnosis of faults in rolling element bearings Jordan-J of Mechanical and Industrial Engineering, vol.3, pp. 260-267 (1986).
- Hongyu Yang, Joseph Mathew and Lin Ma Vibration Feature Extraction Techniques for Fault Diagnosis of Rotating Machinery -A Literature Survey (2003).
- Tandon N and Choudhury A, (1999), A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribology International
- K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman. Return of the devil in the details: Delving deep into convolutional nets. arXiv preprint arXiv:1405.3531, 2014
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