摘要
提出了采用神经网络修正灰色残差组合模型对设备的磨损状态进行预测和诊断分析的方法。通过比较GM(1,1)模型、神经网络模型的预测结果,融合GM(GreyModel)模型与神经网络模型并构建组合模型进行油液光谱分析参数预测,可以克服单个模型所存在的不足。结果证明,该组合模型方法在预测中是可行的,预测的误差最小。
Remnant difference correct combined model of BP neural network was built to predict and diagnose the wear of oil lubricated mechanical equipment. By comparing the prediction results of GM （ 1,1 ） and BP neural network, the combination of GM and neural model is feasible in oil spectral analysis parameter prediction, which can overcome the deficiency of single model and get good effect.
出处
《润滑与密封》
CAS
CSCD
北大核心
2007年第3期172-174,共3页
Lubrication Engineering
