Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution
The early fault characteristics of rolling element bearings carried by vibration signals are quite weak because the signals are generally masked by heavy background noise. To extract the weak fault characteristics of bearings from the signals, an improved spectral kurtosis (SK) method is proposed ba...
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-11-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/15/11/29363 |
| _version_ | 1817996514584166400 |
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| author | Feng Jia Yaguo Lei Hongkai Shan Jing Lin |
| author_facet | Feng Jia Yaguo Lei Hongkai Shan Jing Lin |
| author_sort | Feng Jia |
| collection | DOAJ |
| description | The early fault characteristics of rolling element bearings carried by vibration signals are quite weak because the signals are generally masked by heavy background noise. To extract the weak fault characteristics of bearings from the signals, an improved spectral kurtosis (SK) method is proposed based on maximum correlated kurtosis deconvolution (MCKD). The proposed method combines the ability of MCKD in indicating the periodic fault transients and the ability of SK in locating these transients in the frequency domain. A simulation signal overwhelmed by heavy noise is used to demonstrate the effectiveness of the proposed method. The results show that MCKD is beneficial to clarify the periodic impulse components of the bearing signals, and the method is able to detect the resonant frequency band of the signal and extract its fault characteristic frequency. Through analyzing actual vibration signals collected from wind turbines and hot strip rolling mills, we confirm that by using the proposed method, it is possible to extract fault characteristics and diagnose early faults of rolling element bearings. Based on the comparisons with the SK method, it is verified that the proposed method is more suitable to diagnose early faults of rolling element bearings. |
| first_indexed | 2024-04-14T02:23:16Z |
| format | Article |
| id | doaj.art-9a07da7a1c334575bdee36387f5a41ff |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T02:23:16Z |
| publishDate | 2015-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9a07da7a1c334575bdee36387f5a41ff2022-12-22T02:17:57ZengMDPI AGSensors1424-82202015-11-011511293632937710.3390/s151129363s151129363Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis DeconvolutionFeng Jia0Yaguo Lei1Hongkai Shan2Jing Lin3State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, No. 28 Xianning West Road, Xi’an 710049, ChinaState Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, No. 28 Xianning West Road, Xi’an 710049, ChinaState Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, No. 28 Xianning West Road, Xi’an 710049, ChinaState Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, No. 28 Xianning West Road, Xi’an 710049, ChinaThe early fault characteristics of rolling element bearings carried by vibration signals are quite weak because the signals are generally masked by heavy background noise. To extract the weak fault characteristics of bearings from the signals, an improved spectral kurtosis (SK) method is proposed based on maximum correlated kurtosis deconvolution (MCKD). The proposed method combines the ability of MCKD in indicating the periodic fault transients and the ability of SK in locating these transients in the frequency domain. A simulation signal overwhelmed by heavy noise is used to demonstrate the effectiveness of the proposed method. The results show that MCKD is beneficial to clarify the periodic impulse components of the bearing signals, and the method is able to detect the resonant frequency band of the signal and extract its fault characteristic frequency. Through analyzing actual vibration signals collected from wind turbines and hot strip rolling mills, we confirm that by using the proposed method, it is possible to extract fault characteristics and diagnose early faults of rolling element bearings. Based on the comparisons with the SK method, it is verified that the proposed method is more suitable to diagnose early faults of rolling element bearings.http://www.mdpi.com/1424-8220/15/11/29363maximum correlated kurtosis deconvolutionspectral kurtosisrolling element bearingearly fault diagnosis |
| spellingShingle | Feng Jia Yaguo Lei Hongkai Shan Jing Lin Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution Sensors maximum correlated kurtosis deconvolution spectral kurtosis rolling element bearing early fault diagnosis |
| title | Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution |
| title_full | Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution |
| title_fullStr | Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution |
| title_full_unstemmed | Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution |
| title_short | Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution |
| title_sort | early fault diagnosis of bearings using an improved spectral kurtosis by maximum correlated kurtosis deconvolution |
| topic | maximum correlated kurtosis deconvolution spectral kurtosis rolling element bearing early fault diagnosis |
| url | http://www.mdpi.com/1424-8220/15/11/29363 |
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