摘要
机械设备的磨损对设备故障和使用维护非常重要。基于铁谱技术提出了磨粒识别的灰色关联度模型和BP神经网络模型组合优化识别的方法,试验和应用表明组合模型优于2种单一模型,较大地提高了预测精度。
The of mechanical equipment plays an important role in its faults and maintenance. Based on ferro-graphy an optimization identifying method combining the gray correlation coefficient model with BP neural network model of debris recognition is presented in the paper. Tests and application proved that the combination model is superior to either model and enhance the predicted precision.
出处
《中国航海》
CSCD
北大核心
2005年第2期73-75,共3页
Navigation of China
基金
山东省自然基金资助项目 (Y2 0 0 4F15 )
关键词
机械学
磨损故障
研究
智能诊断
灰色神经网络
铁谱技术
Mechanical engineering
Abrasion fault
Research
Intelligence diagnosis
Grey neural network
Ferro-graphy
