摘要
1550冷轧机作为钢铁企业的重要设备,其工况要求24 h不间断运行,对此类设备进行寿命预测研究,实施适时的停机检修和工作任务安排,对钢铁企业具有重大意义。首先建立了基于神经网络的设备剩余寿命预测过程模型;其次分析了神经网络在设备剩余寿命预测领域的应用,分别构建了基于BP神经网络的设备状态识别与剩余寿命预测模型;最后以某钢铁企业1550连轧机齿轮箱的寿命预测为例,验证了该方法的有效性。
As the key equipment of iron and steel industry, 1550 cold rolling mill is required for 24-hour continuous running. So it＇s of great significance to do life-prediction research in order to implement timely halt service and task arrangement on this requirement. Firstly, the model of residual life prediction process based on neural network is established. Secondly, this paper analyzed the application of neural network on the field of equipment＇s residual life prediction and established the model of equipment＇s condition identification and residual life prediction model based on BP neural network. Lastly, this method is validated through the example of the residual life prediction for gear box of 1550 cold rolling mill.
出处
《机电一体化》
2010年第7期20-23,92,共5页
Mechatronics
基金
上海市科学技术委员会科研计划资助项目(08DZ1120900
09DZ1122000)
上海市网络化制造与企业信息化重点实验室开放基金(KF200911)
