Fault state prediction method and device based on improved neural network

The invention discloses a fault state prediction method and device based on an improved neural network, and the method comprises the steps: obtaining the fault feature data of a hydraulic system, wherein the fault feature data is the feature data for predicting a fault state; and inputting the fault feature data into an SCG-WBP neural network model for prediction to obtain a corresponding fault state, the SCG-WBP neural network model being a model obtained by improving a BP neural network by using a wavelet function and a proportional conjugate gradient algorithm and predicting the fault statebased on the fault feature data. The invention aims to provide a fault state prediction method and device based on an improved neural network so as to rapidly and accurately predict the fault state of a hydraulic system. 本申请公开了一种基于改进神经网络的故障状态预测的方法及装置，本申请的方法包括获取液压系统的故障特征数据，所述故障特征数据为用于预测故障状态的特征数据；将所述故障特征数据输入到SCG-WBP神经网络模型中进行预测，得到对应的故障状态，所述SCG-WBP神经网络模型为利用小波函数和成比例的共轭梯度算法对BP神经网路进行改进得到的基于故障特征数据预测故障状态的模型。本申请是为了提供一种基于改进神经网络的故障状