Next Article in Journal
Bio-Mimic Optimization Strategies in Wireless Sensor Networks: A Survey
Previous Article in Journal
Optical Sensing Using Dark Mode Excitation in an Asymmetric Dimer Metamaterial
Sensors 2014, 14(1), 283-298; doi:10.3390/s140100283

Fault Detection of Roller-Bearings Using Signal Processing and Optimization Algorithms

Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 100-715, Korea
* Author to whom correspondence should be addressed.
Received: 18 November 2013 / Revised: 18 December 2013 / Accepted: 23 December 2013 / Published: 24 December 2013
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [720 KB, uploaded 21 June 2014]   |   Browse Figures


This study presents a fault detection of roller bearings through signal processing and optimization techniques. After the occurrence of scratch-type defects on the inner race of bearings, variations of kurtosis values are investigated in terms of two different data processing techniques: minimum entropy deconvolution (MED), and the Teager-Kaiser Energy Operator (TKEO). MED and the TKEO are employed to qualitatively enhance the discrimination of defect-induced repeating peaks on bearing vibration data with measurement noise. Given the perspective of the execution sequence of MED and the TKEO, the study found that the kurtosis sensitivity towards a defect on bearings could be highly improved. Also, the vibration signal from both healthy and damaged bearings is decomposed into multiple intrinsic mode functions (IMFs), through empirical mode decomposition (EMD). The weight vectors of IMFs become design variables for a genetic algorithm (GA). The weights of each IMF can be optimized through the genetic algorithm, to enhance the sensitivity of kurtosis on damaged bearing signals. Experimental results show that the EMD-GA approach successfully improved the resolution of detectability between a roller bearing with defect, and an intact system.
Keywords: roller-bearing; fault detection; minimum entropy deconvolution; genetic algorithm roller-bearing; fault detection; minimum entropy deconvolution; genetic algorithm
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
MDPI and ACS Style

Kwak, D.-H.; Lee, D.-H.; Ahn, J.-H.; Koh, B.-H. Fault Detection of Roller-Bearings Using Signal Processing and Optimization Algorithms. Sensors 2014, 14, 283-298.

View more citation formats

Related Articles

Article Metrics

For more information on the journal, click here


Cited By

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert