Paper
29 December 1998 Birefringent torque sensor for motors
Dukki Chung, Francis L. Merat, Fred M. Discenzo, James S. Harris
Author Affiliations +
Proceedings Volume 3520, Three-Dimensional Imaging, Optical Metrology, and Inspection IV; (1998) https://doi.org/10.1117/12.334339
Event: Photonics East (ISAM, VVDC, IEMB), 1998, Boston, MA, United States
Abstract
Birefringent optical materials can be used to convert mechanical strain into fringe patterns of optical intensity which have typically been used to measure surface stains or stresses. In this paper a system will be described that uses a photoelastic transducer, linear sensor array, and neural network image processing to estimate the load torque for stationary and rotating motor shafts up to 1500 rpm. A photoelastic polymer coupling is attached to the shaft, and illuminated by polarized light. As the shaft torque varies the photoelastic plastic coupling experiences torsional strain. This results in a corresponding 2D fringe pattern when viewed through an optical polarizer. The strain that causes this observed pattern in a complex function of the applied torque applied to the shaft. A neural network is trained with the fringe patterns corresponding to calibrated load torques as measured by a laboratory strain gauge torque sensor. Experimental results show that the neural network torque estimator can accurately estimate the applied torque for both static and rotating shafts.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Dukki Chung, Francis L. Merat, Fred M. Discenzo, and James S. Harris "Birefringent torque sensor for motors", Proc. SPIE 3520, Three-Dimensional Imaging, Optical Metrology, and Inspection IV, (29 December 1998); https://doi.org/10.1117/12.334339
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Sensors

Neural networks

Photoelasticity

Fringe analysis

Image sensors

Image processing

Error analysis

Back to Top