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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.


Simulation signals (a) and its wavelet scalogram (b).
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f3-sensors-14-02071: Simulation signals (a) and its wavelet scalogram (b).

Mentions: According to the QPC model introduced in Equation (8), the simulation signal model is as follows [24,35]:(16)x(t)=[cos(2πf1t+φ1)+cos(2πf2t+φ2)+A(cos(2π(f1+f2)t+(φ1+φ2)))+(1−A)(cos(2π(f1+f2)t+φ3))]e[−(t−(nT+0.125))2/(1/600)]+n(t)where f1 and f2 are the coupled frequency, φ1 and φ2 are the initial phase distributed within (−π, π] uniformly, n(t) is white Gaussian noise with zero-mean and unit variance, T is the period of the signal and A is the coupling coefficient. The simulation signal in Figure 3a is configurated as f1 = 80 Hz, f2 = 270 Hz, A = 1 and T = 0.25 s with the sampling rate 1,000 Hz, and its wavelet scalogram is shown in Figure 3b.


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

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

Simulation signals (a) and its wavelet scalogram (b).
© Copyright Policy
Related In: Results  -  Collection

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

f3-sensors-14-02071: Simulation signals (a) and its wavelet scalogram (b).
Mentions: According to the QPC model introduced in Equation (8), the simulation signal model is as follows [24,35]:(16)x(t)=[cos(2πf1t+φ1)+cos(2πf2t+φ2)+A(cos(2π(f1+f2)t+(φ1+φ2)))+(1−A)(cos(2π(f1+f2)t+φ3))]e[−(t−(nT+0.125))2/(1/600)]+n(t)where f1 and f2 are the coupled frequency, φ1 and φ2 are the initial phase distributed within (−π, π] uniformly, n(t) is white Gaussian noise with zero-mean and unit variance, T is the period of the signal and A is the coupling coefficient. The simulation signal in Figure 3a is configurated as f1 = 80 Hz, f2 = 270 Hz, A = 1 and T = 0.25 s with the sampling rate 1,000 Hz, and its wavelet scalogram is shown in Figure 3b.

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.