To deal with the problem of weak prediction and performance evaluation capabilities of traditional prediction and evaluation methods, a method of health state prediction and performance evaluation of belt conveyor based on Dynamic Bayesian Network (DBN) is proposed. First, the belt conveyor sensor monitoring data are preprocessed to obtain the feature data set with labels. At the same time, qualitative and quantitative analyses and interval discretization are carried out from belt conveyor fault-causing elements to construct the DBN network. Then, the sample data are applied to the DBN network for training, and the DBN-based prediction and performance evaluation model is established. Finally, taking the real-time monitoring data of a belt conveyor in an underground mine as an example, a DBN-based belt conveyor health prediction and evaluation model is constructed to evaluate and predict the health of the equipment. The results show that the model could identify different operating conditions and failure modes and further improves the prediction accuracy.

中文翻译：

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基于动态贝叶斯网络的地下采矿带式输送机健康状态预测与性能评估

针对传统预测和评估方法预测能力和评估能力较弱的问题，提出了一种基于动态贝叶斯网络（DBN）的皮带机健康状态预测和绩效评估方法。首先，对皮带输送机传感器监控数据进行预处理，以获得带有标签的特征数据集。同时，对皮带输送机的故障原因进行定性和定量分析以及区间离散化，以构建DBN网络。然后，将样本数据应用于DBN网络进行训练，并建立了基于DBN的预测和性能评估模型。最后，以地下矿山皮带输送机的实时监控数据为例，构建了基于DBN的皮带输送机运行状况预测和评估模型，以评估和预测设备的运行状况。结果表明，该模型可以识别不同的工况和故障模式，并进一步提高了预测精度。