基于Hilbert-Huang变换和BP神经网络的核级电动阀门退化趋势预测

Degradation Trend Prediction of Nuclear-level Electric Valve Based on Hilbert-Huang Transform and BP Neural Network

核级电动阀门服役环境恶劣,极易发生退化失效。为准确预测核级电动阀门性能退化趋势,采用Hilbert-Huang变换(HHT)和反向传播神经网络(BPNN)相结合的方法(HHT-BPNN)对核级电动阀门的退化状态进行预测。本文采用某次核级电动阀门可靠性试验的振动信号对电动阀门退化趋势进行预测,结果显示该方法能准确预测出核级电动阀门的3种退化状态,且其相对误差在可接受范围内。研究表明HHT能够有效提取信号的退化信息,BPNN能够准确预测核级电动阀门的退化趋势,HHT-BPNN预测方法能有效解决核级电动阀门性能退化预测困难的问题。

Due to the harsh service environment of nuclear-level electric valves,degradation and failure are easy to occur.Therefore,in order to accurately predict the performance degradation trend of nuclear-level electric valves,this study adopts a method based on Hilbert-Huang transform(HHT)and BP neural network(BPNN)combined method(HHT-BPNN)to predict the degradation state of nuclear-level electric valve.In this paper,the vibration signal of a nuclear-level electric valve reliability test is used to predict the degradation trend of the electric valve.The results show that the method can accurately predict the three degradation states of the nuclear-level electric valve,and the relative error is within the acceptable range.The analysis and research results show that HHT can effectively extract the degradation information of the signal,and BPNN can accurately predict the degradation trend of nuclear-level electric valves.The HHT-BPNN prediction method can effectively solve the difficulty of predicting the performance degradation of nuclear-level electric valves.