基于小波包分解与随机森林的离心泵故障诊断OA北大核心CSTPCD

Aiming at the difficulties of on-line fault diagnosis of centrifugal pumps in nuclear power plants,a fault diagnosis method based on wavelet pack decomposition and random forest is proposed.Firstly,the wavelet pack decomposition was used to decompose the vibration signal in the radial vertical direction of the centrifugal pump motor drive end into three layers,and the sub-band energy features were extracted.Then,the time-domain statistical features were extracted based on the waveform data of centrifugal pump vibration signal,and combined with wavelet packet energy features as inputs for the random forest model.Finally,the random forest model was trained with centrifugal pump vibration dataset collected from vibration test,and the centrifugal pump fault diagnosis model was formed.This model was compared with machine learning models such as support vector machine,logistic regression,K-nearest neighbor and Gaussian Naive Bayes on the same centrifugal pump vibration dataset.The results showed that the constructed model could accurately identify different operating states of the centrifugal pump,such as normal operation,impeller damage,impeller blockage and motor bearing fault,and exhibited better classification performance.The fault diagnosis method based on wavelet packet decomposition and random forest can effectively extract features from vibration signals and realize fault classification,which has certain feasibility and effectiveness for on-line fault intelligent diagnosis of centrifugal pumps in nuclear power plants.