Crack Fault Diagnosis and Location Method for a Dual-Disk Hollow Shaft Rotor System Based on the Radial Basis Function Network and Pattern Recognition Neural Network
Abstract The crack fault is one of the most common faults in the rotor system, and researchers have paid close attention to its fault diagnosis. However, most studies focus on discussing the dynamic response characteristics caused by the crack rather than estimating the crack depth and position base...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-03-01
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| Series: | Chinese Journal of Mechanical Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s10033-023-00856-y |
| _version_ | 1797865344922025984 |
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| author | Yuhong Jin Lei Hou Zhenyong Lu Yushu Chen |
| author_facet | Yuhong Jin Lei Hou Zhenyong Lu Yushu Chen |
| author_sort | Yuhong Jin |
| collection | DOAJ |
| description | Abstract The crack fault is one of the most common faults in the rotor system, and researchers have paid close attention to its fault diagnosis. However, most studies focus on discussing the dynamic response characteristics caused by the crack rather than estimating the crack depth and position based on the obtained vibration signals. In this paper, a novel crack fault diagnosis and location method for a dual-disk hollow shaft rotor system based on the Radial basis function (RBF) network and Pattern recognition neural network (PRNN) is presented. Firstly, a rotor system model with a breathing crack suitable for a short-thick hollow shaft rotor is established based on the finite element method, where the crack’s periodic opening and closing pattern and different degrees of crack depth are considered. Then, the dynamic response is obtained by the harmonic balance method. By adjusting the crack parameters, the dynamic characteristics related to the crack depth and position are analyzed through the amplitude-frequency responses and waterfall plots. The analysis results show that the first critical speed, first subcritical speed, first critical speed amplitude, and super-harmonic resonance peak at the first subcritical speed can be utilized for the crack fault diagnosis. Based on this, the RBF network and PRNN are adopted to determine the depth and approximate location of the crack respectively by taking the above dynamic characteristics as input. Test results show that the proposed method has high fault diagnosis accuracy. This research proposes a crack detection method adequate for the hollow shaft rotor system, where the crack depth and position are both unknown. |
| first_indexed | 2024-04-09T23:07:42Z |
| format | Article |
| id | doaj.art-a5935064f9154f91a9041a87172b15a3 |
| institution | Directory Open Access Journal |
| issn | 2192-8258 |
| language | English |
| last_indexed | 2024-04-09T23:07:42Z |
| publishDate | 2023-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Chinese Journal of Mechanical Engineering |
| spelling | doaj.art-a5935064f9154f91a9041a87172b15a32023-03-22T10:37:26ZengSpringerOpenChinese Journal of Mechanical Engineering2192-82582023-03-0136111810.1186/s10033-023-00856-yCrack Fault Diagnosis and Location Method for a Dual-Disk Hollow Shaft Rotor System Based on the Radial Basis Function Network and Pattern Recognition Neural NetworkYuhong Jin0Lei Hou1Zhenyong Lu2Yushu Chen3School of Astronautics, Harbin Institute of TechnologySchool of Astronautics, Harbin Institute of TechnologyInstitute of Dynamics and Control Science, Shandong Normal UniversitySchool of Astronautics, Harbin Institute of TechnologyAbstract The crack fault is one of the most common faults in the rotor system, and researchers have paid close attention to its fault diagnosis. However, most studies focus on discussing the dynamic response characteristics caused by the crack rather than estimating the crack depth and position based on the obtained vibration signals. In this paper, a novel crack fault diagnosis and location method for a dual-disk hollow shaft rotor system based on the Radial basis function (RBF) network and Pattern recognition neural network (PRNN) is presented. Firstly, a rotor system model with a breathing crack suitable for a short-thick hollow shaft rotor is established based on the finite element method, where the crack’s periodic opening and closing pattern and different degrees of crack depth are considered. Then, the dynamic response is obtained by the harmonic balance method. By adjusting the crack parameters, the dynamic characteristics related to the crack depth and position are analyzed through the amplitude-frequency responses and waterfall plots. The analysis results show that the first critical speed, first subcritical speed, first critical speed amplitude, and super-harmonic resonance peak at the first subcritical speed can be utilized for the crack fault diagnosis. Based on this, the RBF network and PRNN are adopted to determine the depth and approximate location of the crack respectively by taking the above dynamic characteristics as input. Test results show that the proposed method has high fault diagnosis accuracy. This research proposes a crack detection method adequate for the hollow shaft rotor system, where the crack depth and position are both unknown.https://doi.org/10.1186/s10033-023-00856-yHollow shaft rotorBreathing crackRadial basis function networkPattern recognition neural networkMachine learning |
| spellingShingle | Yuhong Jin Lei Hou Zhenyong Lu Yushu Chen Crack Fault Diagnosis and Location Method for a Dual-Disk Hollow Shaft Rotor System Based on the Radial Basis Function Network and Pattern Recognition Neural Network Chinese Journal of Mechanical Engineering Hollow shaft rotor Breathing crack Radial basis function network Pattern recognition neural network Machine learning |
| title | Crack Fault Diagnosis and Location Method for a Dual-Disk Hollow Shaft Rotor System Based on the Radial Basis Function Network and Pattern Recognition Neural Network |
| title_full | Crack Fault Diagnosis and Location Method for a Dual-Disk Hollow Shaft Rotor System Based on the Radial Basis Function Network and Pattern Recognition Neural Network |
| title_fullStr | Crack Fault Diagnosis and Location Method for a Dual-Disk Hollow Shaft Rotor System Based on the Radial Basis Function Network and Pattern Recognition Neural Network |
| title_full_unstemmed | Crack Fault Diagnosis and Location Method for a Dual-Disk Hollow Shaft Rotor System Based on the Radial Basis Function Network and Pattern Recognition Neural Network |
| title_short | Crack Fault Diagnosis and Location Method for a Dual-Disk Hollow Shaft Rotor System Based on the Radial Basis Function Network and Pattern Recognition Neural Network |
| title_sort | crack fault diagnosis and location method for a dual disk hollow shaft rotor system based on the radial basis function network and pattern recognition neural network |
| topic | Hollow shaft rotor Breathing crack Radial basis function network Pattern recognition neural network Machine learning |
| url | https://doi.org/10.1186/s10033-023-00856-y |
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