Fault Feature Extraction of Gear Crack based on QPSO-Volterra
In view of the limitation of current mainstream methods of gear crack fault detection (that is only using the system response as research object, and seldom considering the input effect on fault feature extraction), and taking into account its dynamic characteristics as a kind of typical nonlinear s...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Transmission
2019-07-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2019.07.002 |
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| Summary: | In view of the limitation of current mainstream methods of gear crack fault detection (that is only using the system response as research object, and seldom considering the input effect on fault feature extraction), and taking into account its dynamic characteristics as a kind of typical nonlinear system, by applying Volterra series theory into different states of gear meshing transmission system, for fully taking the advantage of Volterra series, which can use both the input and output data of system to describe the nonlinear characteristics of system. Meanwhile, using the high global search capability of QPSO algorithm, QPSO algorithm is used to identify the time domain kernel of gear meshing transmission system’s Volterra model(referred to as GIRF). The simulation results show that the high-order GIRFs are very sensitive to the nonlinear characteristics of the system caused by the gear crack failure, and can effectively characterize and distinguish the nonlinear dynamic characteristics of the gear meshing transmission system under different conditions. The simulation results achieve the expected purpose. |
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| ISSN: | 1004-2539 |