Conveying belt tearing and deviation detection method, device and equipment and storage medium

Abstract

The invention relates to a conveying belt tearing and deviation detection method and device, equipment and a storage medium, and the method comprises the steps: obtaining the video data of a conveying belt, and constructing an image database; inputting the data of the image database into the deep neural network model for training, and outputting abnormal image data features; training to obtain a support vector machine model under a hyper-parameter combination based on the hyper-parameter combination of the support vector machine model, wherein the hyper-parameter combination enables the classification accuracy to be the highest; the deep neural network model and the support vector machine model are used for detecting the video data of the conveying belt; when a preset condition is met, the deep neural network model outputs a feature vector z, and the current video data of the conveying belt is judged to be abnormal; and the support vector machine model outputs an abnormal result of conveying belt tearing or conveying belt deviation based on the feature vector z. The problem that the recognition accuracy of the tearing or deviation scene of the conveying belt is low is solved. The method has the effect of improving the recognition accuracy.

Description

technical field [0001] The present application relates to the technical field of industrial automation, in particular to a method, device, equipment and storage medium for detecting conveyor belt tearing and deviation. Background technique [0002] At present, the conveyor belt is an important tool for the power plant to carry thermal coal, and it is a necessary condition for the safe and reliable operation of the power plant. However, during the long-term coal transportation process, due to the alternating load changes, fatigue damage and other reasons, the conveyor belt will have problems such as tearing and deviation. In severe cases, it will affect the normal operation of the factory and even threaten the workers. job security. [0003] Specifically, conveyor belt tearing refers to the phenomenon of conveyor belt tearing caused by friction damage, metal jamming, external pressure, high-speed operation, etc. during the process of transporting coal, which will lead to the...

AI Technical Summary

Problems solved by technology

However, during the long-term coal transportation process, due to the alternating load changes, fatigue damage and other reasons, the conveyor belt will have problems such as tearing and deviation. In severe cases, it will affect the normal operation of the factory and even threaten the workers. job safety

[0003] Specifically, conveyor belt tearing refers to the phenomenon of conveyor belt tearing caused by friction damage, metal jamming, external pressure, high-speed operation, etc. during the process of transporting coal, which will lead to thermal coal leakage and continuous expansion of cracks. In severe cases, it will cause major accidents such as machine failure, production stagnation, and casualties, which will pose a major threat to the safe operation of the coal mine transportation system

The existing conveyor belt tear detection methods mainly include manual detection, sensor detection, and tear detection methods based on image processing, but manual detection methods cannot detect tears in time and stop transportation; sensor detection is easily damaged, resulting in detection failure, and accurate Low performance, stability and reliability; tear detection methods based on image processing can usually only detect tears in a single scene, lack early prediction of tear trends, and have low versatility and reliability

[0004] Conveyor belt deviation means that the distance between the actual centerline and the theoretical centerline of the conveyor belt is too large during operation, which will cause thermal coal to accumulate at the end and form side leakage, causing environmental pollution, increasing cleaning workload, affecting production efficiency and the use of the conveyor belt service life, and it will lead to safety production accidents

The existing conveyor belt deviation detection methods mainly include manual detection, photoelectric detection and visual image detection methods, but the manual detection method is inefficient and cannot detect the deviation in time and carry out shutdown maintenance; photoelectric detection equipment is expensive and difficult to promote; visual image The detection method depends on the mathematical model, and is greatly affected by the complex environment, which easily leads to low detection accuracy

[0005] In view of the related technologies mentioned above, the inventor believes that there is a defect that the recognition accuracy of the existing conveyor belt tearing or deviation scene is low

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