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A wavelet bicoherence-based quadratic nonlinearity feature for translational axis condition monitoring.

Li Y, Wang X, Lin J, Shi S - Sensors (Basel) (2014)

Bottom Line: 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.All the results show that the performance of the proposed feature is much better than that of original condition monitoring features.

View Article: PubMed Central - PubMed

Affiliation: School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China. liyongmec@stu.xjtu.edu.cn.

ABSTRACT
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.

No MeSH data available.


Servomotor torque of X-axis at different number of the days before maintenance.
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f7-sensors-14-02071: Servomotor torque of X-axis at different number of the days before maintenance.

Mentions: Figure 7 shows the calculated servomotor torque (left panels) of X-axis and its spectrum (right panels), at 192 days, 62 days 34 days and 2 days before maintenance, respectively. It can be seen from the left panels of Figure 7 that the servomotor torque fluctuates for each data sample. During the last 34 days before maintenance, some impulses appear in the last half of the ball screw travel. As shown in the right panels of Figure 7, the spectrum plot of each data sample shows that the same dominating spectrum peaks appear at the servomotor's rotary frequency and the first, second and third harmonics of the reducer meshing frequency, with very slight changes in the peaks' value. At the same time, a resonance appears at the frequencies band between 330–370 Hz, during the last 62 days before maintenance. Similar results occur in the spectrum of almost every data sample. Thus, it is not easy to describe the degradation progress by directly using the change of the characteristic frequencies.


A wavelet bicoherence-based quadratic nonlinearity feature for translational axis condition monitoring.

Li Y, Wang X, Lin J, Shi S - Sensors (Basel) (2014)

Servomotor torque of X-axis at different number of the days before maintenance.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3958269&req=5

f7-sensors-14-02071: Servomotor torque of X-axis at different number of the days before maintenance.
Mentions: Figure 7 shows the calculated servomotor torque (left panels) of X-axis and its spectrum (right panels), at 192 days, 62 days 34 days and 2 days before maintenance, respectively. It can be seen from the left panels of Figure 7 that the servomotor torque fluctuates for each data sample. During the last 34 days before maintenance, some impulses appear in the last half of the ball screw travel. As shown in the right panels of Figure 7, the spectrum plot of each data sample shows that the same dominating spectrum peaks appear at the servomotor's rotary frequency and the first, second and third harmonics of the reducer meshing frequency, with very slight changes in the peaks' value. At the same time, a resonance appears at the frequencies band between 330–370 Hz, during the last 62 days before maintenance. Similar results occur in the spectrum of almost every data sample. Thus, it is not easy to describe the degradation progress by directly using the change of the characteristic frequencies.

Bottom Line: 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.All the results show that the performance of the proposed feature is much better than that of original condition monitoring features.

View Article: PubMed Central - PubMed

Affiliation: School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China. liyongmec@stu.xjtu.edu.cn.

ABSTRACT
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.

No MeSH data available.