皮带运输状况综合监测分析数据

1.本数据为皮带运输过程中异常情况的告警、分析数据，应用于皮带运输行业；通过对工作中的皮带温度、音频进行监测，对各物流公司及其他使用目标皮带的企业进行实时预警，准确提示故障发生位置及原因，及时维修，减少因皮带故障带来的附加损失。2.该数据可用于皮带及托辊供应商对产品性能进行定向优化减少产品故障率，有效避免因产品设计缺陷造成的设备停产，从而提高企业产能。1.数据采集：皮带运输系统是一个专门用于监测各企业皮带运输情况的数据收集、分析、处理系统，通过AI算法来检测分析皮带运输情况并及时预警制动，该AI算法实时对皮带状况进行监测，通过可见光摄像头和红外摄像头对场地中三条沿线的托辊进行区域温度采集，利用二维码扫描器读取场地位置信息。利用声音采集器采集当前位置环境的音频。2.数据处理：通过采集到的数据判断正在运行中的正常托辊的温度，同时将红外摄像头采集到的图像转换为热力图，输入模型中;利用公式db = 20 * log10 （spl / 20 μpa）计算音频强度。3、模型处理：实时将托辊温度、音频强度与正常托辊运行过程中的托辊温度和音频强度对比，温度和音频同时超过阈值即判定为托辊异常。同时为进一步提高鲁棒性，计算热力图中温度异常的区域，4：数据应用：最后将识别到的异常的托辊位置与托辊报警信息上传至前端。

1. This dataset contains alarm and analysis data for abnormal situations during conveyor belt transportation, and is applied to the conveyor belt transportation industry. By monitoring the temperature and audio of operating conveyor belts, it provides real-time early warnings for logistics companies and other enterprises using target conveyor belts, accurately indicates the location and cause of faults, enables timely maintenance, and reduces additional losses caused by conveyor belt failures.
2. This dataset can be used by conveyor belt and idler roller suppliers to perform targeted optimization of product performance, reduce product failure rates, effectively avoid equipment shutdown caused by product design defects, thereby improving enterprise production capacity.
1. Data Collection: The conveyor belt transportation system is a specialized data collection, analysis and processing system for monitoring the conveyor belt transportation status of various enterprises. It uses AI algorithms to detect and analyze conveyor belt transportation conditions, and conduct real-time early warning and braking. The AI algorithm monitors the conveyor belt status in real time: it collects regional temperatures of idler rollers along three routes in the site through visible light cameras and infrared cameras, uses QR code scanners to read site location information, and uses audio collectors to collect environmental audio at the current location.
2. Data Processing: The collected data is used to determine the temperature of normal operating idler rollers. Meanwhile, the images collected by infrared cameras are converted into heatmaps and input into the model; the audio intensity is calculated using the formula db = 20 * log10 (spl / 20 μPa).
3. Model Processing: The temperature and audio intensity of idler rollers are compared with those of normal operating idler rollers in real time. If both the temperature and audio exceed the threshold at the same time, the idler roller is judged to be abnormal. To further improve robustness, the temperature anomaly regions in the heatmap are also calculated.
4. Data Application: Finally, the identified abnormal idler roller positions and idler roller alarm information are uploaded to the front-end interface.

搜集汇总

数据集介绍

特点

该数据集为皮带运输状况综合监测分析数据，包含501条记录，每日更新。数据通过AI算法实时监测皮带运输过程中的温度和音频异常，应用于故障预警和产品优化，有效减少因皮带故障带来的损失。