分布式光纖安防檢測系統(tǒng)的信號識別方法研究
發(fā)布時間：2018-12-23 08:00
【摘要】：隨著社會的快速發(fā)展,通信電纜、輸油管道、輸氣管道、高壓電網(wǎng)以及軍用、民用安防系統(tǒng)等基礎設備、設施的安全監(jiān)測等問題已日趨成為影響經(jīng)濟發(fā)展和社會穩(wěn)定的重要因素。因此,針對各種危及電纜、管道安全及安防系統(tǒng)中非法入侵行為的及時發(fā)現(xiàn)和有效定位具有重要的研究價值和現(xiàn)實意義。本課題來源于與天津大學合作的國家973項目“光纖智能傳感網(wǎng)實驗平臺關鍵技術及其應用的基礎研究”課題,負責其中“惡劣環(huán)境對連續(xù)分布式傳感網(wǎng)偏振及定位影響研究”子項目。本文以提高Mach-Zehnder干涉儀光纖安防系統(tǒng)擾動事件識別精確度為目標,采用小波多分辨率分析和神經(jīng)網(wǎng)絡等手段,對采集得到的分布式光纖傳感安防系統(tǒng)的監(jiān)測數(shù)據(jù)進行信號特征提取以及信號識別等研究,實現(xiàn)了對外界環(huán)境的下雨、敲擊、攀爬等信號的識別,對提高安防監(jiān)測系統(tǒng)中入侵行為的準確定位提供了研究基礎。本文主要完成了以下幾方面的工作：(1)采用小波變換,對分布式光纖傳感安防系統(tǒng)的監(jiān)測數(shù)據(jù)實現(xiàn)了多分辨率信息提取,通過小波能譜實現(xiàn)了信號特征的向量提取,獲得了能較好反映各種安全事件的本質(zhì)特征,對下雨、敲擊、攀爬等信號實現(xiàn)了初步識別。(2)深入理解神經(jīng)網(wǎng)絡結構,將BP神經(jīng)網(wǎng)絡識別方法用于對安防檢測信號類型的識別。設計了4種改進后的BP神經(jīng)網(wǎng)絡的MATLAB程序,并運用獲得的樣本數(shù)據(jù)的特征量對其分別進行訓練,對訓練結果進行比較分析,選取最佳BP網(wǎng)絡訓練方法。(3)對實驗現(xiàn)場采集的監(jiān)測數(shù)據(jù)進行了大量實驗研究,證明了算法的有效性及可靠性,基于BP神經(jīng)網(wǎng)絡的識別率達到90%。
[Abstract]:With the rapid development of society, communication cables, oil pipelines, gas pipelines, high-voltage power grids, military and civilian security systems and other basic equipment, Safety monitoring of facilities has become an important factor affecting economic development and social stability. Therefore, it is of great research value and practical significance to detect and locate the illegal intrusion behavior in the cable, pipeline safety and security system. This topic comes from the national 973 project "key Technology and basic Research on the Application of Optical Fiber Intelligent Sensor Network experiment platform", which is in cooperation with Tianjin University. Responsible for the "adverse environment on the continuous distributed sensor network polarization and positioning research" subproject. In order to improve the accuracy of disturbance event identification in Mach-Zehnder interferometer optical fiber security system, wavelet multi-resolution analysis and neural network are used in this paper. The signal feature extraction and signal recognition of the monitoring data of distributed optical fiber sensing security system are studied, and the recognition of rain, knock, climbing and other signals in the outside environment is realized. It provides a research basis for improving the accurate location of intrusion behavior in security monitoring system. The main work of this paper is as follows: (1) Multiresolution information is extracted from the monitoring data of distributed optical fiber sensor security system by wavelet transform, and vector extraction of signal features is realized by wavelet spectrum. The essential characteristics of various security events are obtained, and the initial recognition of the signals such as rain, knocking, climbing and so on is achieved. (2) the neural network structure is deeply understood. The BP neural network recognition method is used to identify the type of security detection signal. Four improved MATLAB programs of BP neural network are designed, and the training results are compared and analyzed by using the characteristic quantity of the obtained sample data. The optimal BP network training method is selected. (3) A large number of experimental studies are carried out on the monitoring data collected from the experiment site, which proves the validity and reliability of the algorithm. The recognition rate based on BP neural network reaches 90 points.
【學位授予單位】：大連海事大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TP274;TN253
本文編號：2389659
[Abstract]:With the rapid development of society, communication cables, oil pipelines, gas pipelines, high-voltage power grids, military and civilian security systems and other basic equipment, Safety monitoring of facilities has become an important factor affecting economic development and social stability. Therefore, it is of great research value and practical significance to detect and locate the illegal intrusion behavior in the cable, pipeline safety and security system. This topic comes from the national 973 project "key Technology and basic Research on the Application of Optical Fiber Intelligent Sensor Network experiment platform", which is in cooperation with Tianjin University. Responsible for the "adverse environment on the continuous distributed sensor network polarization and positioning research" subproject. In order to improve the accuracy of disturbance event identification in Mach-Zehnder interferometer optical fiber security system, wavelet multi-resolution analysis and neural network are used in this paper. The signal feature extraction and signal recognition of the monitoring data of distributed optical fiber sensing security system are studied, and the recognition of rain, knock, climbing and other signals in the outside environment is realized. It provides a research basis for improving the accurate location of intrusion behavior in security monitoring system. The main work of this paper is as follows: (1) Multiresolution information is extracted from the monitoring data of distributed optical fiber sensor security system by wavelet transform, and vector extraction of signal features is realized by wavelet spectrum. The essential characteristics of various security events are obtained, and the initial recognition of the signals such as rain, knocking, climbing and so on is achieved. (2) the neural network structure is deeply understood. The BP neural network recognition method is used to identify the type of security detection signal. Four improved MATLAB programs of BP neural network are designed, and the training results are compared and analyzed by using the characteristic quantity of the obtained sample data. The optimal BP network training method is selected. (3) A large number of experimental studies are carried out on the monitoring data collected from the experiment site, which proves the validity and reliability of the algorithm. The recognition rate based on BP neural network reaches 90 points.
【學位授予單位】：大連海事大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TP274;TN253
【參考文獻】
相關期刊論文 前5條
1 蔣立輝;張峰;劉向明;;Mach-Zehnder干涉型長周界預警系統(tǒng)入侵目標定位[J];傳感器與微系統(tǒng);2011年04期
2 周正仙;段紹輝;田杰;王曉華;徐幫聯(lián);;分布式光纖振動傳感器及振動信號模式識別技術研究[J];光學儀器;2013年06期
3 王科俊,李國斌;幾種變學習率的快速BP算法比較研究[J];哈爾濱工程大學學報;1997年03期
4 周泓,汪樂宇,陳祥獻;虛擬儀器系統(tǒng)軟件結構的設計[J];計算機自動測量與控制;2000年01期
5 丁吉;趙杰;萬遂人;孫小菡;;基于小波包變換的光纖擾動信號模式識別[J];微計算機信息;2011年02期
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