Research On Pattern Recognition Method For Pipeline Abnormal State Monitoring By Φ-OTDR

China in the new era has developed and leapfrogged again and again,creating epic miracles on earth,and at the same time there is the rapid development of deep learning and machine learning,and more and more intelligent equipment has entered our lives.Optical fiber has special functions such as long life,mechanical operation resistance,oxidation resistance,etc.,and can also ensure a high level of working capacity under high heat,strong electromagnetic radiation and extremely harsh conditions.The DAS system using Phase Sensitive Optical Time-domain Reflectometer(Φ-OTDR)technology has the advantages of fast response,long detection distance and high dynamic sensitivity.On the other hand,pipelines,as the fifth largest mode of transportation,occupy a very important position in the construction of energy infrastructure equipment in the future.Once a pipeline leakage occurs,it will not only affect the normal supply work,but also cause natural disasters such as fires,so pipeline safety cannot be ignored.At present,the application of optical fiber sensing technology in pipeline abnormal condition monitoring is a very important research direction,so this paper combines optical fiber sensing technology with pattern recognition to optimize abnormal state localization and classification recognition in pipeline safety monitoring.In terms of pipeline abnormal state location,the false negative rate and false alarm rate of pipeline abnormal state location are reduced to 0 by using the proposed sliding window positioning algorithm.In terms of disturbance signal recognition,the proposed classification algorithm based on binary tree can achieve an average recognition accuracy of 95.77%.The main work of this paper is as follows:Firstly,under the research and development status and development prospect ofΦ-OTDR distributed optical fiber vibration sensing technology,the existing disturbance signal positioning methods of Φ-OTDR are analyzed and studied,and its advantages and disadvantages are expounded,which provides a theoretical basis for the subsequent optimization of positioning algorithms.Secondly,on the basis of the existing Φ-OTDR disturbance signal localization method,a sliding window positioning method is introduced,which analyzes the changes of amplitude data in time and space in real time according to the stationary characteristics of amplitude data on short time scales.Through experiments,the spatiotemporal monitoring data of the temperature difference between inside and outside the pipe and the flow rate at different positions of the abnormal state are obtained,and the optimal values of the abnormal state diagnosis window k and sliding window NS are determined,which greatly reduces the false alarm rate of the system and realizes the identification and positioning of abnormal pipeline states under different conditions.Finally,according to the abnormal vibration of the pipeline,a total of five kinds of disturbance signals were selected: no disturbance,extreme weather such as rain and snow,artificial walking and pedaling,electric trolley passing,and heavy objects falling,and after obtaining the original data,data preprocessing and feature engineering were carried out in turn,and a total of 26 features were extracted.The classification of pipeline disturbance signals based on voting method is proposed,when 80% of the sample data was selected as the training data,the accuracy of abnormal vibration recognition of these five pipelines was 98.72%,96.89%,92.78%,88.25% and 96.67%,respectively.Aiming at the problem that a large number of weak classifiers in the voting method may lead to long operation time and poor classification effect,a pipe disturbance signal classification method based on binary coding is proposed.When 80% of the sample data is selected as the training data,the accuracy of abnormal vibration recognition of these five pipelines is 99.13%,97.16%,95.70%,88.62%,and 95.29%,respectively.Compared with the voting method,the advantage is to reduce the number of weak classifiers and reduce the operation time,but the disadvantage is that the classification effect of individual patterns is reduced.In order to balance the relationship between the number of weak classifiers and the classification accuracy,a pipe disturbance signal classification algorithm based on binary tree is proposed,and binary coding is combined with binary tree to realize a binary coding method based on binary tree.When 80% of the sample data is selected as the training data,the accuracy of abnormal vibration recognition of these five pipelines is 99.60%,98.30%,95.29%,90.37%,and 95.27%,respectively.Compared with the previous two classification algorithms,the classification effect of this algorithm is the best,and the number of weak classifiers is much smaller than the number of weak classifiers required by the voting method.