Weak Fault Feature Extraction of Rolling Bearings Based on an Improved Kurtogram
Kurtograms have been verified to be an efficient tool in bearing fault detection and diagnosis because of their superiority in extracting transient features. However, the short-time Fourier Transform is insufficient in time-frequency analysis and kurtosis is deficient in detecting cyclic transients....
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-09-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/16/9/1482 |
| _version_ | 1798042198053224448 |
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| author | Xianglong Chen Fuzhou Feng Bingzhi Zhang |
| author_facet | Xianglong Chen Fuzhou Feng Bingzhi Zhang |
| author_sort | Xianglong Chen |
| collection | DOAJ |
| description | Kurtograms have been verified to be an efficient tool in bearing fault detection and diagnosis because of their superiority in extracting transient features. However, the short-time Fourier Transform is insufficient in time-frequency analysis and kurtosis is deficient in detecting cyclic transients. Those factors weaken the performance of the original kurtogram in extracting weak fault features. Correlated Kurtosis (CK) is then designed, as a more effective solution, in detecting cyclic transients. Redundant Second Generation Wavelet Packet Transform (RSGWPT) is deemed to be effective in capturing more detailed local time-frequency description of the signal, and restricting the frequency aliasing components of the analysis results. The authors in this manuscript, combining the CK with the RSGWPT, propose an improved kurtogram to extract weak fault features from bearing vibration signals. The analysis of simulation signals and real application cases demonstrate that the proposed method is relatively more accurate and effective in extracting weak fault features. |
| first_indexed | 2024-04-11T22:32:07Z |
| format | Article |
| id | doaj.art-7ae2fa7ca3ed4b0ea189d648df437169 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:32:07Z |
| publishDate | 2016-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7ae2fa7ca3ed4b0ea189d648df4371692022-12-22T03:59:20ZengMDPI AGSensors1424-82202016-09-01169148210.3390/s16091482s16091482Weak Fault Feature Extraction of Rolling Bearings Based on an Improved KurtogramXianglong Chen0Fuzhou Feng1Bingzhi Zhang2Department of Mechanical Engineering, Academy of Armored Forces Engineering, Beijing 100072, ChinaDepartment of Mechanical Engineering, Academy of Armored Forces Engineering, Beijing 100072, ChinaBeijing Special Vehicle Research Institute, Beijing 100072, ChinaKurtograms have been verified to be an efficient tool in bearing fault detection and diagnosis because of their superiority in extracting transient features. However, the short-time Fourier Transform is insufficient in time-frequency analysis and kurtosis is deficient in detecting cyclic transients. Those factors weaken the performance of the original kurtogram in extracting weak fault features. Correlated Kurtosis (CK) is then designed, as a more effective solution, in detecting cyclic transients. Redundant Second Generation Wavelet Packet Transform (RSGWPT) is deemed to be effective in capturing more detailed local time-frequency description of the signal, and restricting the frequency aliasing components of the analysis results. The authors in this manuscript, combining the CK with the RSGWPT, propose an improved kurtogram to extract weak fault features from bearing vibration signals. The analysis of simulation signals and real application cases demonstrate that the proposed method is relatively more accurate and effective in extracting weak fault features.http://www.mdpi.com/1424-8220/16/9/1482correlated kurtosisredundant second generation wavelet package transformkurtogramweak fault diagnosis |
| spellingShingle | Xianglong Chen Fuzhou Feng Bingzhi Zhang Weak Fault Feature Extraction of Rolling Bearings Based on an Improved Kurtogram Sensors correlated kurtosis redundant second generation wavelet package transform kurtogram weak fault diagnosis |
| title | Weak Fault Feature Extraction of Rolling Bearings Based on an Improved Kurtogram |
| title_full | Weak Fault Feature Extraction of Rolling Bearings Based on an Improved Kurtogram |
| title_fullStr | Weak Fault Feature Extraction of Rolling Bearings Based on an Improved Kurtogram |
| title_full_unstemmed | Weak Fault Feature Extraction of Rolling Bearings Based on an Improved Kurtogram |
| title_short | Weak Fault Feature Extraction of Rolling Bearings Based on an Improved Kurtogram |
| title_sort | weak fault feature extraction of rolling bearings based on an improved kurtogram |
| topic | correlated kurtosis redundant second generation wavelet package transform kurtogram weak fault diagnosis |
| url | http://www.mdpi.com/1424-8220/16/9/1482 |
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