Dust concentration vision measurement based on moment of inertia in gray level-rank co-occurrence matrix

TL;DR

This paper introduces a novel vision-based dust concentration measurement method using the Moment of Inertia in Gray level-Rank Co-occurrence Matrix, achieving high accuracy and low cost in industrial environments.

Contribution

The paper develops a new measurement approach based on GRCM and establishes a Polynomial computational model linking dust concentration and Moment of Inertia.

Findings

Measurement error within 9%

Applicable range 0.5-1000 mg/m3

Advantages over traditional methods in error and cost

Abstract

To improve the accuracy of existing dust concentration measurements, a dust concentration measurement based on Moment of inertia in Gray level-Rank Co-occurrence Matrix (GRCM), which is from the dust image sample measured by a machine vision system is proposed in this paper. Firstly, a Polynomial computational model between dust Concentration and Moment of inertia (PCM) is established by experimental methods and fitting methods. Then computing methods for GRCM and its Moment of inertia are constructed by theoretical and mathematical analysis methods. And then developing an on-line dust concentration vision measurement experimental system, the cement dust concentration measurement in a cement production workshop is taken as a practice example with the system and the PCM measurement. The results show that measurement error is within 9%, and the measurement range is 0.5-1000 mg/m3. Finally, comparing with the filter membrane weighing measurement, light scattering measurement and laser measurement, the proposed PCM measurement has advantages on error and cost, which can be provided a valuable reference for the dust concentration vision measurements.