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Sensors 2014, 14(2), 2071-2088; doi:10.3390/s140202071
Article

A Wavelet Bicoherence-Based Quadratic Nonlinearity Feature for Translational Axis Condition Monitoring

1
, 1
, 1,2,*  and 1
Received: 2 December 2013; in revised form: 21 January 2014 / Accepted: 21 January 2014 / Published: 27 January 2014
(This article belongs to the Section Physical Sensors)
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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.
Keywords: condition monitoring; wavelet bicoherence; quadratic nonlinearity; translational axis system condition monitoring; wavelet bicoherence; quadratic nonlinearity; translational axis system
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Li, Y.; Wang, X.; Lin, J.; Shi, S. A Wavelet Bicoherence-Based Quadratic Nonlinearity Feature for Translational Axis Condition Monitoring. Sensors 2014, 14, 2071-2088.

AMA Style

Li Y, Wang X, Lin J, Shi S. A Wavelet Bicoherence-Based Quadratic Nonlinearity Feature for Translational Axis Condition Monitoring. Sensors. 2014; 14(2):2071-2088.

Chicago/Turabian Style

Li, Yong; Wang, Xiufeng; Lin, Jing; Shi, Shengyu. 2014. "A Wavelet Bicoherence-Based Quadratic Nonlinearity Feature for Translational Axis Condition Monitoring." Sensors 14, no. 2: 2071-2088.



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