Analysis Of Pipeline Loss Detection Based On Vibration Principle And Improved SVM

The main purpose of pipeline leak detection is to troubleshoot water supply networks,working pipelines in industrial systems,etc.to prevent pipeline leakage from causing waste of resources or affecting normal production processes.The accuracy of pipeline leak detection directly affects the accuracy of troubleshooting.Therefore,pipeline leak detection technology is of great significance.In this thesis,through the collection and analysis of pipeline vibration acceleration data before and after the leak,the support vector machine and parameter optimization algorithms are used to complete the training and testing of the leak detection model to find the model with the better pipeline leak detection effect.The main contents include the acquisition and analysis of pipeline vibration acceleration data,and the realization of pipeline leakage detection using support vector machines.In the pipeline vibration acceleration data acquisition part,a vibration acceleration measurement device based on the Internet of Things technology is designed to collect pipeline vibration acceleration signals and upload the data collected by multiple measurement devices to the local through Lo Ra(Long Range Radio long-range radio technology).The gateway is aggregated into the cloud platform for storage through the gateway.Considering that the computing and processing capabilities of Io T devices are limited,Lo Ra needs to compress the data before data transmission.The compression algorithm selects the adaptive differential pulse code modulation compression algorithm(ADPCM)by analyzing the signal.After the data collection is completed,the vibration acceleration signals before and after the pipeline leakage are analyzed to find out the main parameter changes of the acceleration signals before and after the pipeline leakage,which is convenient for the subsequent selection of appropriate feature quantities for support vector machine training pipeline leakage detection models.In the support vector machine(SVM)pipeline leak detection part,the principle of support vector machine and different parameter optimization algorithms are introduced.The original support vector machine model and the support vector machine model optimized by different optimization algorithms are compared for pipeline leak detection.As a result,the better pipeline leak detection model was found.The pipeline leak detection model obtained by training is tested and verified,and the results show that whether it is the original data of vibration acceleration or the data encoded and compressed by the compression algorithm,the original SVM model is optimized by using the particle swarm optimization algorithm.The leakage detection model can achieve the best detection effect.The detection accuracy of the original data is 98%,and the detection accuracy of the compressed and decompressed data is 90.07%.The pipeline leakage detection is well realized.