基于ELM和连续过程神经网络的抽油机工况诊断  

Working condition diagnosis of oil pump machine based on continuous process neural network and extreme learning machine

作　　者：刘志刚[1] 许少华 李盼池[1] 

机构地区：[1]东北石油大学计算机与信息技术学院,黑龙江大庆163318 [2]山东科技大学信息科学与工程学院,山东青岛266590

出　　处：《计算机工程与科学》2017年第10期1934-1940,共7页Computer Engineering & Science

基　　金：国家自然科学基金(61170132);黑龙江省教育厅基金(11551015)

摘　　要：普通神经网络进行抽油机工况诊断时存在诊断精度偏低的问题,提出选用连续过程神经元网络作为诊断模型,特征输入选取能直接反映示功图几何形态特征的位移和载荷两种连续信号。为提高模型学习速度,提出过程神经网络的极限学习算法,将训练转换为最小二乘问题,根据样本输入计算隐层输出矩阵,使用SVD法求解Moore-Penrose广义逆,最后计算隐层输出权值。通过诊断实验,模型学习速度提升5倍左右,与普通神经网络进行对比,诊断精度提高8个百分点左右,验证了方法的有效性。To tackle the shortcoming of common neural networks of low diagnostic accuracy in theworking condition diagnosis of the oil pump machine, we propose a method that selects continuous process neural networks as the diagnostic model. Because the geometric shape characteristics of the indi-cator diagram are directly reflected by the continuous signals of move and load, we select them as the feature input of the diagnostic model. In order to increase the learning speed,w e learning algorithm to process neural networks. The model training process is converted to a least square problem. The output matrix of the hidden layer is calculated according to the samprose generalized inverse is solved by the SVD algorithm and then the output weights of the hidden layer are calculated. Diagnostic experiments show that the learning speed is increased by about 5 times, the diagnostic accuracy is improved by about 8 percentage in comparison with common neural networks,and the validity of the method is verified.

关 键 词：工况诊断 过程神经元网络 极限学习 MOORE-PENROSE广义逆 网络训练 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]