基于Baseline模型的刮板输送机健康评估

Health Assessment of Scraper Conveyor Based on Baseline Model

对刮板输送机进行可靠健康评估是保证工作面高效开采的关键。针对某煤矿综采工作面刮板输送机提出一种基于Baseline模型的健康评估方法。该方法采用长短时记忆(LSTM)深度神经网络建立运行参数的Baseline模型,预测正常的数值和故障发生上、下限阈值。在此基础上建立了真实值与预测值的6种偏差计算方法和设备健康评估机制。最后融合机头电机、机尾电机、机头减速器和机尾减速器的评估结果,完成刮板输送机运行健康综合评估。

The health assessment of scraper conveyer is one of the keys to ensure the efficient mining of working face.A health assessment method based on Baseline model was proposed for scraper conveyer in a working face of a coal mine.In the method,the long short-term memory(LSTM)deep neural network was used to establish Baseline model of operating parameters,and the normal values,the upper and lower thresholds of failure were predicted.Based on those,six kinds of deviation calculation methods between real value and predicted value,and equipment health assessment mechanism were established.Finally,the health comprehensive assessment of scraper conveyer was produced by integrating the assessment results of head motor,tail motor,head reducer and tail reducer.