hydraulic pump life prediction GM （1,1） model neural network

  本文关键词：基于改进灰色神经网络的液压泵寿命预测，由笔耕文化传播整理发布。

基于改进灰色神经网络的液压泵寿命预测

Life Prediction of Hydraulic Pump Based on an Improved Grey Neural Network

[1] [2] [3] [4]

He Qingfei Chen Guiming Chen Xiaohu Yao Chunjiang （ The Second Artillery Engineering College, Xi＇ an, 710025）

第二炮兵工程学院,西安,710025

文章摘要：改进了GM（1，1）模型，提高了其精度和适应范围；将改进的GM（1，1）模型与神经网络预测模型相结合来构建灰色神经网络组合预测模型；提出了基于支持向量机的液压泵寿命特征启发式搜索策略，以液压泵寿命特征参数特征集的交叉验证错误率为评价指标，从液压泵的特征参数（振动、压力、流量、温度、油液信息等）中选取寿命特征因子；运用小波阈值降噪法进行降噪处理，提取典型的小波包能量特征作为模型的输入。以齿轮泵为例，将改进的灰色神经网络预测模型与原始GM（1，1）模型和改进GM（1，1）模型比较可知，灰色神经网络预测模型预测精度最高，，达到98．42％。

Abstr：A life prediction method of hydraulic pump based on improved grey neural network was presented for the shortcomings of low precision forecasting model. Firstly, a new model was proposed based on the combination of the initial condition and the background value to improve the precision of the grey forecasting model. A SVM- based hydraulic pump lifetime feature heuristic elimination strategy was put forward, and the evaluation criterion of cross—validating error rate was adopted to select life feature form hydraulic pump features（vibration, pressure, flux, temperature, oil, and so on）. Feature signals were de—noised by wavelet threshold de—noising method. Then representative energy features were selected by wavelet pocket energy spectrum algorithm. Taking gear pump as an example, the improved grey neural network model has higher precision than original GM（1,1） model and improved GM（1,1） model.

文章关键词：

Keyword：:hydraulic pump life prediction GM （1,1） model neural network support vectormachine（SVM）

课题项目：国防预研基金资助项目（9140A27020309JB4701）;第二炮兵工程学院科技创新基金资助项目（XY2010JJB38）

  本文关键词：基于改进灰色神经网络的液压泵寿命预测，由笔耕文化传播整理发布。