Automatic Calibration System for Digital-Display Vibrometers Based on Machine Vision

Abstract

Considering the low efficiency during the process of traditional calibration for digital-display vibrometers, an automatic calibration system for vibrometers based on machine vision is developed. First, an automatic vibration control system is established on the basis of a personal computer, and the output of a vibration exciter on which a digital-display vibrometer to be calibrated is installed, is automatically adjusted to vibrate at a preset vibration level and a preset frequency. Then the display of the vibrometer is captured by a digital camera and identified by means of image recognition. According to the vibration level of the exciter measured by a laser interferometer and the recognized display of the vibrometer, the properties of the vibrometer are calculated and output by the computer. Image recognition algorithms for the display of the vibrometer with a high recognition rate are presented, and the recognition for vibrating digits and alternating digits is especially analyzed in detail. Experimental results on the built-up system show that the prposed image recognition methods are very effective and the system could liberate operators from boring and intense calibration work for digital-display vibrometers.

Automated laser engraving system for the calibration and manufacturing of nonlinear scales for electrical measuring instruments

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Automated Calibration System for Digital Multimeters Not Equipped with a Communication Interface

“…Although the articles present algorithms and software solutions, most authors do not state how effective they are [ 21 ]. These works, where the topic of effectiveness is considered, provide information about the lack of 100% effectiveness [ 22 ] or about effectiveness at the level of 99.33% [ 23 ] or 99.75% [ 24 ]. Although these are relatively high efficiencies, in the solution presented in this article, thanks to focusing only on seven-segment displays, we managed to achieve 100% result recognition efficiency.…”

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