- Title
- Machine fault severity estimation based on adaptive wavelet nodes selection and SVM
- Creator
- Yaqub, Muhammad; Gondal, Iqbal; Kamruzzaman, Joarder
- Date
- 2011
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/76169
- Identifier
- vital:7492
- Identifier
-
https://doi.org/10.1109/ICMA.2011.5986279
- Identifier
- ISBN:9781424481149
- Abstract
- The study is focused on estimating the severity level of the bearing faults which helps in determining the residual life of the equipment and planned maintenance. A novel technique, adaptive severity estimation model (ASEM) is proposed based on adaptive selection of wavelet decomposition nodes and support vector machines. Vibration data from multiple severity levels are used to build the fault estimation model. An adaptive criterion for wavelet decomposition node selection is developed which helps ASEM to achieve robustness in estimating fault severity under varying signal to noise ratio (SNR), a key demand in industrial environment. The simulated data with known severity level is used to parameterize the estimation model. The fault severity estimation performance of ASEM is also validated for the real vibration data and its robustness is gauged under varying SNR conditions.
- Publisher
- IEEE
- Relation
- IEEE International Conference on Mechatronics and Automation (ICMA),Beijing 7 August 2011 to 10 August 2011) p. 1951-1956
- Rights
- This metadata is freely available under a CCO license
- Subject
- Condition monitoring; Estimation theory; Fault diagnosis; Machine bearings; Maintenance engineering; Mechanical engineering computing; Remaining life assessment; Signal processing; Support vector machines; Vibrations; Wavelet transforms
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