Research On Valve Working State Detection In Pressurized Pipeline

The development of the pressurized pipeline transportation industry has entered a golden age.As an indispensable control component in pipeline transportation,valves play an important role in maintaining the smooth operation of pipelines.However,in actual engineering,the valves in the pressurized pipeline are often in a harsh working environment,and most of them are buried in the ground,and it is very inconvenient to overhaul the working state.Over time,the actual working state of the valve deviates from the initial set value,forcing the imbalance of the pipeline pressure to affect the conveying effect.In severe cases,the pipeline will be damaged and bring serious consequences.Therefore,it is very important to timely check the working state of the valve in the pressurized pipeline.Local Mean Decomposition(LMD)is a signal adaptive analysis method used to process transient signals and non-stationary signals.Based on the theory of pressurized pipeline transient flow,the LMD method is used to extract the energy entropy of transient flow pressure wave for the working state of the valve in the pressurized pipeline,and analyze it under the conditions of numerical simulation and experimental conditions respectively.The feasibility and extensiveness of the detection method of the working state of the valve in the pressurized pipeline.Firstly,based on the theory of pipeline transient flow,based on the actual pressure pipeline parameters,combined with the flow characteristics of the transport medium,the characteristic line method is applied to simulate the transient flow of the pipeline.The results show that the transient flow pressure wave is stable and reliable under the condition of fast valve closing.When the pressure wave monitoring point is closer to the end valve,the transient flow pressure waveform has the highest completeness and the longest duration,and the result can be used for detecting the working state of the valve by the LMD method.Secondly,based on the definition of LMD algorithm and energy entropy,the LDL energy entropy feature extraction method is proposed,and the feature extraction analysis is carried out for numerical simulation conditions.The results show that the method can be used for the detection of the working condition of the pressurized pipeline valve.After the LMD energy entropy feature extraction analysis,it is found that the larger the valve opening degree,the larger the energy entropy,and the maximum energy entropy when the valve opening reaches the maximum.Because the energy distribution in the pipeline is relatively uniform at this time,the smaller the valve opening degree is,the larger the local resistance coefficient is,and the greater the energy loss at the valve,the smaller the energy entropy at this time.Finally,design and build the pipeline valve working state detection test bench,use the experiment to study the different operating conditions of the pipeline,the different operating conditions of the valve,use the most favorable pressure data collected,and use the wavelet shrinkage noise reduction method for the acquisition.The signal is denoised,and then the LMD energy entropy method is used to extract the characteristic of the pressure wave signal,so that the valve working state is tested under experimental conditions.The comparison results show that in the experimental pipeline system,the LMD energy entropy method is also applicable to the transient pressure wave collected by the pressure sensor,but the experimentally measured transient flow pressure wave needs to be denoised,otherwise the detection error is large.In summary,the LMD energy entropy feature extraction method can be used for the valve working state detection of pressurized pipelines,not only the pressure wave simulated by the transient flow of the pressurized pipeline,but also the experimentally measured transient flow pressure wave.However,it needs to be denoised,and the research results in this paper provide a theoretical basis for applying the method to practical engineering.