Large-scale carrying belt tearing fault intelligent detection method based on dynamic images

Abstract

The invention relates to a large-scale carrying belt tearing fault intelligent detection method based on dynamic images, which comprises the following steps: step 1, determining a mounting position ofan intelligent high-speed industrial camera for a large-scale carrying belt transportation system; step 2, preprocessing a belt operation image collected by the camera; step 3, segmenting the belt tearing image; step 4, extracting characteristic parameters of the belt tearing image; step 5, calculating the connected domain area of the belt tearing image, performing fault grade division accordingto the belt tearing area of the industrial site, obtaining the belt tearing threshold value, and when the corresponding actual tearing area of the carrying belt is yes, judging a second-level fault; and when the actual tearing area of the corresponding carrying belt is greater than the actual tearing area, judging as a first-stage fault; and determining the tearing area and the tearing fault levelof the belt. Compared with the prior art, the method has the advantages of high detection precision, high real-time performance, no need of manual intervention and the like.

Description

Technical field [0001] The invention relates to the field of intelligent detection of large-scale carrying belt equipment in an industrial automation production line, and in particular to an intelligent detection method for tearing faults of large-scale carrying belts based on dynamic images. Background technique [0002] Carrying belts are widely used in coal mine production, metallurgy and other industrial fields, because the transported materials are often mixed with various sharp impurities, and these impurities can cause damage to the belt. At the slightest level, the belt will be torn and the wire rope core will be broken. At the worst, the belt will be broken horizontally, and even long-distance longitudinal tearing will seriously reduce the service life of the carrier belt. [0003] At present, most belt tear detection methods are based on the contact switch detection method of mechanical conductive silicone rubber strips. On the one hand, the physical equipment is fixed at...

AI Technical Summary

Problems solved by technology

The method adopted by this detection method is rough, the detection accuracy is low, and the design of the imaging system is complex. The processing of dynamic images and the detection of moving object faults are computationally complex and time-consuming. It is difficult to meet the rapid detection of industrial production faults. The goal of exclusion, especially in the coal mine production line, the carrying belt is very important for the transportation of coal mines. How to quickly and accurately detect whether the carrying belt is torn or not is the expectation of production technicians for many years

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