Paper
26 January 2016 Measurement uncertainty sources analysis for parasitic time grating sensors
Hongtao Yang, Jiao Zhou, Bin Fan, Yetai Fei, Donglin Peng, Tianfeng Wu
Author Affiliations +
Proceedings Volume 9903, Seventh International Symposium on Precision Mechanical Measurements; 99030E (2016) https://doi.org/10.1117/12.2213833
Event: Seventh International Symposium on Precision Mechanical Measurements, 2015, Xia'men, China
Abstract
The signal quality of traveling wave and the measurement accuracy of parasitic time grating can be improved by optimiz ing its structure. This optimization process can be guided through building the electrical traveling wave equation with respect to the structure and the traveling wave signal generation principle. Based on Ansoft Maxwell simulation, the important electromagnetic parameters and the main uncertainty sources were analyzed and determined respectively. In the simulation parameters such as the excitation signal frequency, the gap width, the relative area of the probe, the coils number, the excitation signal amplitude and the core length were set to different values. It can be seen from the simulation results that excitation signal frequency, the gap width, the relative area between the probe and the rotor are the major factors to influence the angular measuring accuracy of parasitic time grating sensor. Meanwhile, the coils number, the excitation signal amplitude and the core length are the secondary factors. The analysis result can be utilized to optimize the structure of parasitic time grating and correct measurement error.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hongtao Yang, Jiao Zhou, Bin Fan, Yetai Fei, Donglin Peng, and Tianfeng Wu "Measurement uncertainty sources analysis for parasitic time grating sensors", Proc. SPIE 9903, Seventh International Symposium on Precision Mechanical Measurements, 99030E (26 January 2016); https://doi.org/10.1117/12.2213833
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KEYWORDS
Error analysis

Sensors

Magnetism

Signal processing

Digital signal processing

Electromagnetism

3D modeling

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