Volume 17, Issue 2 (3-2017)                   Modares Mechanical Engineering 2017, 17(2): 325-332 | Back to browse issues page

XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Rahbar M, Chaibakhsh A. Comparison between empirical mode decomposition and wavelet transform for unbalance detection on rotating machinery using optimized support vector machine. Modares Mechanical Engineering 2017; 17 (2) :325-332
URL: http://mme.modares.ac.ir/article-15-10043-en.html
1- University of Guilan
2- Assistant Prof. / University of Guilan
Abstract:   (4412 Views)
In this study, fair comparisons between the empirical mode decomposition, ensemble empirical mode decomposition and discrete wavelet transform with the mother wavelet function of Meyer and Daubechies, were performed for detecting unbalance faults in a rotating machinery. In order to classify the healthy class from the unbalance classes, a support vector machines that was optimized by particle swarm optimization algorithm, was used. A comparison between the performances of optimized and non-optimized of support vector machines were also carried out. In order to obtained the required data, a rotating machinery fault simulator was developed and vibrational signals were acquired at healthy and unbalance fault conditions by accelerometer sensors. By processing the recorded signals and analysing signal to their frequency components, several statistical features were extracted from each frequency component as input support vector machine for the separation of classes. The obtained results indicated that the discrete wavelet transform with the Meyer mother wavelet, higher success rate than other methods for diagnosing unbalance faults.
Full-Text [PDF 1130 kb]   (6953 Downloads)    
Article Type: Research Article | Subject: Control
Received: 2016/11/4 | Accepted: 2017/01/25 | Published: 2017/02/19

Add your comments about this article : Your username or Email:
CAPTCHA

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.