A Wavelet Bicoherence-Based Quadratic Nonlinearity Feature for Translational Axis Condition Monitoring
The translational axis is one of the most important subsystems in modern machine tools, as its degradation may result in the loss of the product qualification and lower the control precision. Condition-based maintenance (CBM) has been considered as one of the advanced maintenance schemes to achieve...
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| Format: | Article |
| Language: | English |
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MDPI AG
2014-01-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/14/2/2071 |
| _version_ | 1818038679388553216 |
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| author | Yong Li Xiufeng Wang Jing Lin Shengyu Shi |
| author_facet | Yong Li Xiufeng Wang Jing Lin Shengyu Shi |
| author_sort | Yong Li |
| collection | DOAJ |
| description | The translational axis is one of the most important subsystems in modern machine tools, as its degradation may result in the loss of the product qualification and lower the control precision. Condition-based maintenance (CBM) has been considered as one of the advanced maintenance schemes to achieve effective, reliable and cost-effective operation of machine systems, however, current vibration-based maintenance schemes cannot be employed directly in the translational axis system, due to its complex structure and the inefficiency of commonly used condition monitoring features. In this paper, a wavelet bicoherence-based quadratic nonlinearity feature is proposed for translational axis condition monitoring by using the torque signature of the drive servomotor. Firstly, the quadratic nonlinearity of the servomotor torque signature is discussed, and then, a biphase randomization wavelet bicoherence is introduced for its quadratic nonlinear detection. On this basis, a quadratic nonlinearity feature is proposed for condition monitoring of the translational axis. The properties of the proposed quadratic nonlinearity feature are investigated by simulations. Subsequently, this feature is applied to the real-world servomotor torque data collected from the X-axis on a high precision vertical machining centre. All the results show that the performance of the proposed feature is much better than that of original condition monitoring features. |
| first_indexed | 2024-12-10T07:46:34Z |
| format | Article |
| id | doaj.art-2eb37a2a592e44258f541e97829758c5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-12-10T07:46:34Z |
| publishDate | 2014-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2eb37a2a592e44258f541e97829758c52022-12-22T01:57:11ZengMDPI AGSensors1424-82202014-01-011422071208810.3390/s140202071s140202071A Wavelet Bicoherence-Based Quadratic Nonlinearity Feature for Translational Axis Condition MonitoringYong Li0Xiufeng Wang1Jing Lin2Shengyu Shi3School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, ChinaSchool of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, ChinaSchool of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, ChinaSchool of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, ChinaThe translational axis is one of the most important subsystems in modern machine tools, as its degradation may result in the loss of the product qualification and lower the control precision. Condition-based maintenance (CBM) has been considered as one of the advanced maintenance schemes to achieve effective, reliable and cost-effective operation of machine systems, however, current vibration-based maintenance schemes cannot be employed directly in the translational axis system, due to its complex structure and the inefficiency of commonly used condition monitoring features. In this paper, a wavelet bicoherence-based quadratic nonlinearity feature is proposed for translational axis condition monitoring by using the torque signature of the drive servomotor. Firstly, the quadratic nonlinearity of the servomotor torque signature is discussed, and then, a biphase randomization wavelet bicoherence is introduced for its quadratic nonlinear detection. On this basis, a quadratic nonlinearity feature is proposed for condition monitoring of the translational axis. The properties of the proposed quadratic nonlinearity feature are investigated by simulations. Subsequently, this feature is applied to the real-world servomotor torque data collected from the X-axis on a high precision vertical machining centre. All the results show that the performance of the proposed feature is much better than that of original condition monitoring features.http://www.mdpi.com/1424-8220/14/2/2071condition monitoringwavelet bicoherencequadratic nonlinearitytranslational axis system |
| spellingShingle | Yong Li Xiufeng Wang Jing Lin Shengyu Shi A Wavelet Bicoherence-Based Quadratic Nonlinearity Feature for Translational Axis Condition Monitoring Sensors condition monitoring wavelet bicoherence quadratic nonlinearity translational axis system |
| title | A Wavelet Bicoherence-Based Quadratic Nonlinearity Feature for Translational Axis Condition Monitoring |
| title_full | A Wavelet Bicoherence-Based Quadratic Nonlinearity Feature for Translational Axis Condition Monitoring |
| title_fullStr | A Wavelet Bicoherence-Based Quadratic Nonlinearity Feature for Translational Axis Condition Monitoring |
| title_full_unstemmed | A Wavelet Bicoherence-Based Quadratic Nonlinearity Feature for Translational Axis Condition Monitoring |
| title_short | A Wavelet Bicoherence-Based Quadratic Nonlinearity Feature for Translational Axis Condition Monitoring |
| title_sort | wavelet bicoherence based quadratic nonlinearity feature for translational axis condition monitoring |
| topic | condition monitoring wavelet bicoherence quadratic nonlinearity translational axis system |
| url | http://www.mdpi.com/1424-8220/14/2/2071 |
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