Fault Detection of Roller-Bearings Using Signal Processing and Optimization Algorithms
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 ent...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2013-12-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/14/1/283 |
| _version_ | 1798040265175334912 |
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| author | Dae-Ho Kwak Dong-Han Lee Jong-Hyo Ahn Bong-Hwan Koh |
| author_facet | Dae-Ho Kwak Dong-Han Lee Jong-Hyo Ahn Bong-Hwan Koh |
| author_sort | Dae-Ho Kwak |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T22:05:05Z |
| format | Article |
| id | doaj.art-536ab4fefeea49bd90f93c67d764e08c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:05:05Z |
| publishDate | 2013-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-536ab4fefeea49bd90f93c67d764e08c2022-12-22T04:00:45ZengMDPI AGSensors1424-82202013-12-0114128329810.3390/s140100283s140100283Fault Detection of Roller-Bearings Using Signal Processing and Optimization AlgorithmsDae-Ho Kwak0Dong-Han Lee1Jong-Hyo Ahn2Bong-Hwan Koh3Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 100-715, KoreaDepartment of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 100-715, KoreaDepartment of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 100-715, KoreaDepartment of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 100-715, KoreaThis 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.http://www.mdpi.com/1424-8220/14/1/283roller-bearingfault detectionminimum entropy deconvolutiongenetic algorithm |
| spellingShingle | Dae-Ho Kwak Dong-Han Lee Jong-Hyo Ahn Bong-Hwan Koh Fault Detection of Roller-Bearings Using Signal Processing and Optimization Algorithms Sensors roller-bearing fault detection minimum entropy deconvolution genetic algorithm |
| title | Fault Detection of Roller-Bearings Using Signal Processing and Optimization Algorithms |
| title_full | Fault Detection of Roller-Bearings Using Signal Processing and Optimization Algorithms |
| title_fullStr | Fault Detection of Roller-Bearings Using Signal Processing and Optimization Algorithms |
| title_full_unstemmed | Fault Detection of Roller-Bearings Using Signal Processing and Optimization Algorithms |
| title_short | Fault Detection of Roller-Bearings Using Signal Processing and Optimization Algorithms |
| title_sort | fault detection of roller bearings using signal processing and optimization algorithms |
| topic | roller-bearing fault detection minimum entropy deconvolution genetic algorithm |
| url | http://www.mdpi.com/1424-8220/14/1/283 |
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