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Sensors 2014, 14(1), 283-298; doi:10.3390/s140100283
Article

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

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Received: 18 November 2013; in revised form: 18 December 2013 / Accepted: 23 December 2013 / Published: 24 December 2013
(This article belongs to the Section Physical Sensors)
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Abstract: 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.

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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.

AMA 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(1):283-298.

Chicago/Turabian Style

Kwak, Dae-Ho; Lee, Dong-Han; Ahn, Jong-Hyo; Koh, Bong-Hwan. 2014. "Fault Detection of Roller-Bearings Using Signal Processing and Optimization Algorithms." Sensors 14, no. 1: 283-298.


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