Chaotic Characteristic Analysis And Short-term Prediction Of Pumping Station Pipeline Vibration

Vibration of all kinds of pipelines including pumping station pipelines is ubiquitous in engineering practice.There is a wide engineering background for the analysis of pipeline vibration characteristics and vibration prediction.During the long-term operation of pumping station pipelines,natural and artificial factors affect the pipeline vibration to varying degrees.Long-term and irregular pipeline vibration will lead to loosening of pipelines and its subsidiary structures,which will cause catastrophic damage in serious cases.In this paper,the measured vibration responses of the pumping station pipeline in an irrigation area are selected as the research objects.In order to verify the chaotic characteristics of pumping station pipeline vibration,to clearly the pipeline vibration excitation caused the chaotic characteristics,and to realize short-term chaotic prediction of pipeline vibration,the chaotic characteristics and short-term prediction of pipeline vibration are studied based on chaotic theory.The research contents of this paper are mainly divided into the following three aspects:(1)Chaotic characteristic analysis of pumping station pipeline vibration.At present,the chaotic characteristics of the pipeline vibration in pumping station are seldom analyzed at home and abroad.Based on the quantitative analysis methods of chaotic characteristics such as the saturation correlation dimension and the largest Lyapunov exponent,the chaotic characteristics of the pipeline vibration responses at different points under different conditions are analyzed in this paper.The results show that the chaotic characteristics exist in pumping station pipelines under some working conditions.The complexity and chaotic degree of pipeline vibration are different under different locations of pipelines,different operating conditions of units and different vibration directions.There is a common law that the vibration complexity and chaotic characteristics of pumping station pipelines are greatly affected by the vibration of units and the stability of flow regime.(2)Determine the chaotic vibration excitation of pumping station pipeline.Based on the results of previous analysis,the excitation compositions of the pumping station pipeline are determined by spectrum analysis,and the chaotic characteristics of those components are preliminarily judged.In order to effectively separate the vibration excitation components at different time-scale,an improved variational mode decomposition(IVMD) method is introduced to adaptively decompose the vibration signals into a series of IMFs with different scale characteristics.The saturation correlation dimension and the largest Lyapunov exponent are calculated to analyze the chaotic characteristics of the IMFs to determine the chaotic vibration excitation of pumping station pipeline.(3)Establish the short-term chaotic prediction model for pumping station pipeline vibration.Based on the analysis results of chaotic characteristics,the short-term chaotic prediction model of pipeline vibration is established to further extend the application of pipeline vibration responses in the field of chaotic system.In this paper,two short-term prediction methods,Volterra series prediction and RBF(Radial Basis Function) neural network prediction,are selected.Firstly,the validity of the two prediction methods in the field of chaotic prediction is verified by taking Lorenz system x-component as an example.Secondly,based on the analysis results of chaotic characteristics mentioned above,pipeline vibration responses covering the effects of unit operation and fluid state catastrophe are selected.The chaotic short-term prediction of the vibration responses is realized to judge whether the chaotic short-term prediction model in this paper has the universality of the pipeline vibration response.Finally,a short-term chaotic prediction model for the pipeline vibration response is established by comparing the prediction results of the two methods.