Design and Development of Novel Computer Vision-Based Automatic Calibration System for Analog Dial Pressure Gauge

Pressure gauge calibration refers to comparing the standard and test gauge to determine the error percentage. The reading accuracy of the analog dial pressure gauge is found in the conventional method through manual calculations and then tabulating the values in the calibration certificate. The pres...

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Veröffentlicht in:Smart and sustainable manufacturing systems 2022-07, Vol.6 (1), p.131-147
Hauptverfasser: Prasad, S. J. Suji, Indra, J., Thangatamilan, M., Meena, Radhey Shyam, Muthusamy, Suresh, Panchal, Hitesh, Krishnamoorthy, Mahendran, Sadasivuni, Kishor Kumar, Doshi, Manish
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Sprache:eng
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Zusammenfassung:Pressure gauge calibration refers to comparing the standard and test gauge to determine the error percentage. The reading accuracy of the analog dial pressure gauge is found in the conventional method through manual calculations and then tabulating the values in the calibration certificate. The present study is undertaken to increase the accuracy level of the analog dial pressure gauges. A new automated computer-based method is proposed to calibrate the meters more accurately than the conventional method. The indicated value of the pressure gauge is obtained through the angular position of the pointer and the error value is identified by comparing both standard and test gauge values. In this work, Red, Green, Blue (RGB) images of standard and test pressure gauges are acquired through a high-definition camera. After the image processing operations, the pointer orientation of the indicatoris identified, and the indicated values are calculated. The computer-vision-based automatic calibration system is applied in a 0–100-psi (0–689.476 kPa) analog dial pressure gauge, and three trials were performed to determine the accuracy. The computer vision technique improved accuracy, varying from 97 % to 98 % compared with the conventional manual observation method. The observed results have improved repeatability and accuracy with the proposed computer-vision-based system.
ISSN:2520-6478
2572-3928
DOI:10.1520/SSMS20210019