Intelligent safety monitoring and predictive maintenance system for mining equipment
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摘要: 针对现有矿用设备监测数据精度不高、数据分析能力弱、智能化程度不高等问题，提出了一种矿用设备智能安全监测与预知维护系统。该系统通过基于模拟退火算法的智能数据预选模型对矿用设备安全监测数据进行预处理，筛选出阈值范围内的数据，以剔除数据中的异常值；利用基于卷积神经网络的智能安全预知维护模型，使用不同大小的卷积核进行双通道卷积，多样化提取筛选后的数据特征，并与矿用设备历史状态样本数据进行对比，从而判断矿用设备运行状态，进而提供相应的设备预知维护方案。实验结果表明，该系统在矿用设备运行状态判断及预知维护方面具有较高精度。Abstract: In order to solve the problems of low accuracy of monitoring data, weak data analysis capabilities, and low intelligenceof existing mining equipment, an intelligent safety monitoring and predictive maintenance system for mining equipment is proposed.The system preprocesses mining equipment safety monitoring data through an intelligent data pre-selection model based on the simulated annealing algorithm, filters out the data within the threshold range so as to eliminate outliers in the data. The intelligent safety predictive maintenance model based on convolutional neural network is used to perform dual-channel convolution using convolutional kernels of different sizes to diversify the extracted filtered data characteristics. By comparing them with the mining equipment historical status sample data, it is able to judge the mining equipment operation status and then provide corresponding equipment predictive maintenance solutions.The experimental results show that the system has high accuracy in judging the operation status and predictive maintenance of mining equipment.
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期刊类型引用(2)
1. 李雯静，邱莉，林志勇，姚囝，谢展扬. 基于役龄回退模型的露天矿卡车周期预防性维护策略. 煤炭科学技术. 2023(04): 209-214 . 
百度学术
2. 汪杰，李晓华，郑功勋，王春华，余文波. 基于云平台的煤矿智能运维服务系统研究. 煤矿机械. 2023(08): 191-194 . 百度学术
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