LI Sheng, YAO Xin-hua, FU Jian-zhong etc. Power management system for thermoelectric coupling wireless sensing and its application to thermal monitoring of precision spindle[J]. Editorial Office of Optics and Precision Engineering, 2014,22(9): 2389-2398
LI Sheng, YAO Xin-hua, FU Jian-zhong etc. Power management system for thermoelectric coupling wireless sensing and its application to thermal monitoring of precision spindle[J]. Editorial Office of Optics and Precision Engineering, 2014,22(9): 2389-2398 DOI: 10.3788/OPE.20142209.2389.
Power management system for thermoelectric coupling wireless sensing and its application to thermal monitoring of precision spindle
As the thermoelectric coupling self-powering of a wireless sensor has a lower power and a fluctuated voltage
this paper proposes a new Power Management System(PMS) and its optimal time control strategy to allow the self-power wireless sensor to work stably at different spindle speeds. A thermal network model for the tool spindle was established and the input characteristics of thermal generating power management system was analyzed. Then
a circuit topology with more capacitances for the power management system was designed
and the optimized average output power from thermal generating power was obtained by calculating the charging/discharging time parameters of capacitors. An experiment was performed
and it verifies that the wireless sensor works stably at different spindle speeds driven by thermal power generation devices and the power management system. Several schemes for setting charging/discharging time of capacitors were compared
and the superiority of the time control strategy was verified. Finally
the spindle axial thermal deformation models were established for the data from the thermoelectric coupling self-powering wireless sensor and the traditional wired sensor. The results indicate that the wireless sensor monitors the key part of the spindle that the traditional ones can not be installed in
by which the more directional temperature data relative to the thermal deformation of prediction are obtained
and the error of thermal deformation prediction model is deceased about 40%.
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