基于半监督竞争学习过程神经网络的抽油机故障诊断

Fault Diagnosis of Pumping Unit Based on Semi-Supervised Competitive Learning Process Neural Network

利用过程神经元网络对动态时变信号过程模态特征的自适应提取能力,结合半监督学习算法,提出了一种基于半监督学习的网络结构自适应的竞争型过程神经元网络用于示功图识别.网络采用广义离散Fréchet距离作为动态样本间距离的测度,然后直接以离散化的载荷和位移时间序列作为网络输入,在样本标记信息的约束下,采用奖励—惩罚更新规则,根据网络学习目标函数,动态重构竞争层节点,消除网络对初始聚类数的依赖,实现样本的有效聚类.仿真实验结果验证了模型和算法的有效性.

The adaptive extraction ability of process neural networks is used for dynamic time-varying signal＇s model characteristics.Based on this,a semi-supervised competitive learning process neural network with self-adaptive network structure is proposed to recognize patterns in an indicator diagram.Generalized discrete Fréchet distance is used as a metric between time- varying samples,discrete time sequence of the load and displacement is used as direct inputs,and the reward- punishment rule for updating is used to realize effective clustering under the constraint of labeled samples.The dynamic reconstruction of competitive nodes is realized according to the learning objective function of the network to reduce the influence of the initial cluster number.Experimental results show the effectiveness of the model and algorithm.