基于经验模态分解和随机森林的阀门泄漏模式识别方法

Valve Leakage Mode Recognition Method Based on Empirical Mode Decomposition and Random Forest

作为核电站的一类关键设备,阀门泄漏会给系统安全、稳定运行造成影响。利用声发射技术识别阀门泄漏,区分出内、外两种不同泄漏模式有助于后续针对性维修,对维修经济性提升具有重要意义。针对上述问题,本文提出了一种基于经验模态分解(EMD)和随机森林的阀门泄漏模式识别方法。首先,对泄漏声发射信号进行EMD处理以获得处于不同频段的分量信号;其次,对各分量信号进行傅里叶变换获取其频谱,并从频谱中提取谱能量比作为特征;最后,基于随机森林算法建立智能识别模型以实现内漏和外漏的自动识别。利用试验数据对方法进行了验证,结果表明,该方法提取的谱能量比特征能够量化内漏、外漏两种模式下声发射信号频谱差异,建立的基于随机森林的模型能够有效实现两种泄漏模式的识别。

As a critical equipment in nuclear power plants,valve leakage would have an impact on system safety and stable operation.Using acoustic emission technology to identify valve leakage and distinguish between different leakage modes of internal and external leakage is helpful for targeted maintenance in the future,which is of great significance for improving maintenance economy.Valves are key equipments in nuclear power,and there are two main leakage modes.One mode is external leakage,which means that the fluid in the pipeline flows from the inside of the valve to the outside through the flange joint surface or the gap between the valve stem and the packing.Another mode is internal leakage,which refers to the leakage at the sealing surface of the valve disc inside the valve body due to poor closure or structural defects in the valve disc itself.In the case of internal leakage,the fluid in the pipeline cannot be completely cut off.The application of acoustic emission technology can achieve the identification of valve leakage,but it can only identify whether the valve has leaked and cannot identify whether the valve has leaked internally or externally.Distinguishing different leakage modes is of great significance for the subsequent development of maintenance plans.A valve leakage mode recognition method based on empirical mode decomposition(EMD)and random forest was proposed in this paper.Firstly,the leakage acoustic emission signal was decomposed by EMD to obtain component signals in different frequency bands.Secondly,Fourier transform was performed on each component signal to obtain its spectrum,and spectrum energy ratio features were extracted from the spectrum.Due to the wide frequency distribution range of the leakage acoustic emission signal,the number of features is relatively large.Therefore,it is difficult for general logic algorithms to accurately identify the leakage mode.Machine learning algorithms are very suitable for processing multidimensional data,so it can be used to classify valve internal and external leakage.Random forest is one of machine learning algorithms that uses decision trees as classifiers,which has significant advantages in processing data with high feature dimensions.Finally,an intelligent recognition model was established based on the random forest algorithm to achieve automatic identification of internal and external leakage.The method was validated using experimental data,and the results show that multiple component signals are obtained by decomposing the valve leakage acoustic emission signal using EMD,which refines the spectrum differences of the acoustic emission signal under different leakage modes.The spectrum energy ratio of each component signal was used as a feature for valve leakage mode recognition,achieving accurate characterization of valve internal and external leakage.A leakage mode recognition model for electric valves based on random forest was established,achieving accurate identification of different leakage modes.