Detrended Fluctuation Analysis and Hough Transform Based Self-Adaptation Double-Scale Feature Extraction of Gear Vibration Signals
This paper presents the analysis of the vibration time series of a gear system acquired by piezoelectric acceleration transducer using the detrended fluctuation analysis (DFA). The experimental results show that gear vibration signals behave as double-scale characteristics, which means that the sign...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2016/3409897 |
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| _version_ | 1850168963533635584 |
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| author | JiaQing Wang Han Xiao Yong Lv Tao Wang Zengbing Xu |
| author_facet | JiaQing Wang Han Xiao Yong Lv Tao Wang Zengbing Xu |
| author_sort | JiaQing Wang |
| collection | DOAJ |
| description | This paper presents the analysis of the vibration time series of a gear system acquired by piezoelectric acceleration transducer using the detrended fluctuation analysis (DFA). The experimental results show that gear vibration signals behave as double-scale characteristics, which means that the signals exhibit the self-similarity characteristics in two different time scales. For further understanding, the simulation analysis is performed to investigate the reasons for double-scale of gear’s fault vibration signal. According to the analysis results, a DFA double logarithmic plot based feature vector combined with scale exponent and intercept of the small time scale is utilized to achieve a better performance of fault identification. Furthermore, to detect the crossover point of two time scales automatically, a new approach based on the Hough transform is proposed and validated by a group of experimental tests. The results indicate that, comparing with the traditional DFA, the faulty gear conditions can be identified better by analyzing the double-scale characteristics of DFA. In addition, the influence of trend order of DFA on recognition rate of fault gears is discussed. |
| format | Article |
| id | doaj-art-a02bfd9b42d84ad3bec827e2e0c4811a |
| institution | OA Journals |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-a02bfd9b42d84ad3bec827e2e0c4811a2025-08-20T02:20:51ZengWileyShock and Vibration1070-96221875-92032016-01-01201610.1155/2016/34098973409897Detrended Fluctuation Analysis and Hough Transform Based Self-Adaptation Double-Scale Feature Extraction of Gear Vibration SignalsJiaQing Wang0Han Xiao1Yong Lv2Tao Wang3Zengbing Xu4Hubei Province Key Lab of Machine Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, P.O. Box 222, Wuhan, Hubei 430081, ChinaHubei Province Key Lab of Machine Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, P.O. Box 222, Wuhan, Hubei 430081, ChinaHubei Province Key Lab of Machine Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, P.O. Box 222, Wuhan, Hubei 430081, ChinaHubei Province Key Lab of Machine Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, P.O. Box 222, Wuhan, Hubei 430081, ChinaHubei Province Key Lab of Machine Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, P.O. Box 222, Wuhan, Hubei 430081, ChinaThis paper presents the analysis of the vibration time series of a gear system acquired by piezoelectric acceleration transducer using the detrended fluctuation analysis (DFA). The experimental results show that gear vibration signals behave as double-scale characteristics, which means that the signals exhibit the self-similarity characteristics in two different time scales. For further understanding, the simulation analysis is performed to investigate the reasons for double-scale of gear’s fault vibration signal. According to the analysis results, a DFA double logarithmic plot based feature vector combined with scale exponent and intercept of the small time scale is utilized to achieve a better performance of fault identification. Furthermore, to detect the crossover point of two time scales automatically, a new approach based on the Hough transform is proposed and validated by a group of experimental tests. The results indicate that, comparing with the traditional DFA, the faulty gear conditions can be identified better by analyzing the double-scale characteristics of DFA. In addition, the influence of trend order of DFA on recognition rate of fault gears is discussed.http://dx.doi.org/10.1155/2016/3409897 |
| spellingShingle | JiaQing Wang Han Xiao Yong Lv Tao Wang Zengbing Xu Detrended Fluctuation Analysis and Hough Transform Based Self-Adaptation Double-Scale Feature Extraction of Gear Vibration Signals Shock and Vibration |
| title | Detrended Fluctuation Analysis and Hough Transform Based Self-Adaptation Double-Scale Feature Extraction of Gear Vibration Signals |
| title_full | Detrended Fluctuation Analysis and Hough Transform Based Self-Adaptation Double-Scale Feature Extraction of Gear Vibration Signals |
| title_fullStr | Detrended Fluctuation Analysis and Hough Transform Based Self-Adaptation Double-Scale Feature Extraction of Gear Vibration Signals |
| title_full_unstemmed | Detrended Fluctuation Analysis and Hough Transform Based Self-Adaptation Double-Scale Feature Extraction of Gear Vibration Signals |
| title_short | Detrended Fluctuation Analysis and Hough Transform Based Self-Adaptation Double-Scale Feature Extraction of Gear Vibration Signals |
| title_sort | detrended fluctuation analysis and hough transform based self adaptation double scale feature extraction of gear vibration signals |
| url | http://dx.doi.org/10.1155/2016/3409897 |
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