基于改进RBF的采煤机关键零部件寿命预测方法  

Life Prediction Method for Key Components of Shearer Based on Improved RBF

作　　者：杨宏 Yang Hong(Maiduoshan Coal Mine,Ningxia Coal Industry Co.,Ltd.,CHN Energy Group,Yinchuan 750408,China)

机构地区：[1]国家能源集团宁夏煤业有限责任公司麦垛山煤矿,银川750408

出　　处：《煤矿机械》2025年第3期182-184,共3页Coal Mine Machinery

摘　　要：提出了一种基于改进径向基函数(RBF)网络的采煤机关键零部件寿命预测方法。该方法结合了自回归移动平均(ARIMA)模型,优化了RBF网络的性能和收敛速度。通过收集采煤机关键零部件的实际运行数据,构建并训练了一个改进的RBF网络模型。仿真测试结果表明,该模型能够准确地预测关键零部件的寿命,预测值与实际值相差不大,显示出较高的可靠性和实用性。该研究为采煤机的预防性维护和健康管理提供了新的技术手段,有助于提高设备的安全性和延长使用寿命。Proposed a life prediction method for key components of shearer based on the improved radial basis function(RBF)network.This method integrates the autoregressive integrated moving average(ARIMA)model,and optimizes the performance and convergence speed of the RBF network.By collecting actual operational data from key components of shearer,an improved RBF network model was constructed and trained.Simulation test results show that this model can accurately predict the life of key components with minimal discrepancies between predicted and actual values,showcasing high reliability and practicality.This study provides a new technical approach for preventive maintenance and health management of shearer,which contributes to improve equipment safety and extend service life.

关 键 词：采煤机 关键零部件 寿命预测 改进RBF 

分 类 号：TD421.6[矿业工程—矿山机电]