Research On Monitoring Technology Of Tooth Detection Based On Deep Learning

During the mining of a mining electric shovel,the electric shovel bucket is operated for a long time in a complicated physical and mechanical environment,and the teeth will break and the bucket will fall off locally.The dropped part will be mixed into the coal and loaded into the truck.The truck will directly transport the coal to the crushing station.When the falling part is larger than 300 mm,it will cause the crusher or motor to fail,affecting the normal operation of the production,resulting in serious economic loss and waste of manpower and material resources.The mining excavator is bulky and the working environment is complex,which is not convenient for the electric shovel operator to monitor the working status of the bucket teeth by manual observation;and combined with computer vision,the bucket teeth are monitored in real time during the work process with the real-time detection and automatically judges whether the bucket teeth fall off.When the bucket teeth fall off,the alarm message instructs the staff to take timely measures to prevent greater economic losses.Therefore,the failure monitoring of the power shovel teeth has important practical significance for the safe production of open-pit coal mines and economic value.This article mainly introduces a shovel shovel tooth monitoring method based on deep learning,which aims to monitor the shovel shovel teeth in real time,and issues an alarm when the shovel teeth fall off,prompts personnel to take action to avoid greater losses.Firstly,the corresponding background,research status and significance of shovel tooth monitoring are introduced,and a deep learning-based shovel tooth monitoring method is proposed in combination with current popular deep learning technologies.Secondly,the causes of various failure types of the shovel teeth are analyzed.Based on the failure analysis,the overall design scheme of the monitoring system is proposed,and the analysis and introduction are made on both hardware and software.Thirdly,combining the convolutional neural network and deep learning theory,the YOLOv3 algorithm was improved,the system stability and synchronization were optimized,and the network model was constructed and trained.Designed the human-computer interaction interface of the monitoring system to realize data reading,tooth recognition,fault alarm and fault viewing,etc.,and completed the design of the software system.Finally,simulation verification experiments and field actual tests were carried out.The test results show that the shovel tooth monitoring system based on deep learning has a good recognition effect.It not only has fast detection speed and high accuracy,it can help the shovel to monitor the state of the shovel teeth to a certain extent.The alarm put theory into practice and achieved the desired effect.The paper has 88 pictures,4 tables,and 64 references.