本文選題：瓦斯監(jiān)測　切入點：多傳感器信息融合　出處：《中國礦業(yè)大學(xué)》2013年博士論文

【摘要】：瓦斯災(zāi)害防治仍然是我國煤礦安全工作的重中之重。綜合利用布設(shè)在井下空間的各類非接觸式傳感設(shè)備動態(tài)采集的相關(guān)數(shù)據(jù)，對具有突出危險的工作面實現(xiàn)實時跟蹤監(jiān)測和早期診斷預(yù)警，為煤礦及時采取針對性措施，提高監(jiān)控系統(tǒng)可靠性，防范和抑制瓦斯突出、瓦斯積聚和瓦斯爆炸等事故提供決策依據(jù)，是目前煤礦瓦斯安全監(jiān)測系統(tǒng)亟待增強的功能目標。 論文根據(jù)煤與瓦斯突出、瓦斯爆炸等危險性預(yù)測技術(shù)和預(yù)警理論，采用多傳感器信息融合方法，充分挖掘瓦斯、風(fēng)速、電磁輻射、聲發(fā)射等各類傳感數(shù)據(jù)所蘊涵的規(guī)律性知識，發(fā)揮各類傳感器的優(yōu)勢，按照“手段多樣、優(yōu)勢互補、相互驗證、短中長期搭配”的思路，著力構(gòu)建基于多傳感器信息融合的瓦斯安全監(jiān)測預(yù)警系統(tǒng)，實現(xiàn)對井下工作面瓦斯危險的“實時感知、準確辨識、快速響應(yīng)、有效控制”。論文取得的主要研究成果如下： 全面總結(jié)分析了國內(nèi)外煤礦瓦斯安全動態(tài)監(jiān)測手段和突出危險性評價指標的研究成果，包括瓦斯?jié)舛�、電磁輻射、聲發(fā)射等傳感監(jiān)測技術(shù)及突出預(yù)測方法，為發(fā)揮各自優(yōu)勢，實現(xiàn)煤與瓦斯突出多傳感器融合預(yù)警奠定了堅實的理論基礎(chǔ)。 通過現(xiàn)場調(diào)研，分析了煤礦瓦斯災(zāi)害防治實際需求，本著提高監(jiān)測系統(tǒng)效能，降低系統(tǒng)資源消耗的理念，提出了瓦斯監(jiān)測多傳感器信息融合的目標體系、閉環(huán)工作流程、傳感器選用與組織以及各種瓦斯安全動態(tài)監(jiān)測傳感信息融合分析理論的合理運用，從而最終確定了瓦斯監(jiān)測多傳感器信息融合體系總體結(jié)構(gòu)，，重點研究了基于模糊專家系統(tǒng)的瓦斯突出預(yù)測多傳感器信息決策融合方法。 提出了基于時間序列相似性度量的瓦斯超限報警信號辨識方法。基于DTW距離對煤礦采掘工作面瓦斯超限報警時間序列進行了聚類分析。對所獲得的7種典型的時間序列模式，基于分段形態(tài)度量方法，提取了15個特征指標，從中篩選出5個分類效能較強指標，建立了瓦斯超限報警時間序列形態(tài)特征表。在此基礎(chǔ)上提出了一種瓦斯報警信號快速辨識算法。 提出了基于時空相關(guān)分析的煤礦采掘工作面瓦斯監(jiān)測數(shù)據(jù)異常自動識別技術(shù)。定性分析了工作面順風(fēng)流方向瓦斯運移存在的時空異步相關(guān)特性；確定了相關(guān)系數(shù)計算過程中涉及的異步相關(guān)最優(yōu)滯后步長的計算方法和瓦斯氣體涌出后在回風(fēng)巷道中體積分數(shù)隨時空變化的預(yù)測和反演公式；統(tǒng)計計算了8種原因?qū)е碌耐咚箶?shù)據(jù)異常存在的相關(guān)系數(shù)值變化區(qū)間；提出了基于時空相關(guān)分析的工作面瓦斯監(jiān)測數(shù)據(jù)異常識別算法；為提高相關(guān)分析效率，提出了能表達空間拓撲信息的井下瓦斯傳感器層次編碼方法。 提出將工作面瓦斯安全監(jiān)測問題歸類為專家診斷范疇。研究了瓦斯監(jiān)測信息知識發(fā)現(xiàn)方法，提出了瓦斯時間序列聚類分析與知識提取方法；針對瓦斯監(jiān)測多傳感器信息決策融合專家知識庫系統(tǒng)的設(shè)計需求，提出了瓦斯監(jiān)測知識學(xué)習(xí)算法和瓦斯監(jiān)測專家知識的組織存儲策略。最后舉例說明了基于專家系統(tǒng)的工作面瓦斯超限原因識別推理應(yīng)用過程。
[Abstract]:Gas disaster prevention is still the priority among priorities of coal mine safety work in China. Comprehensive utilization of data layout of non-contact dynamic acquisition sensor equipment of all kinds in the underground space, with outburst dangerous to achieve real-time tracking monitoring and early diagnosis and early warning, for the coal mine to take corresponding measures to improve system reliability. The prevention and suppression of gas outburst, provide decision-making basis for gas accumulation and gas explosion accident, is currently the target function of coal mine gas safety monitoring system needs to be enhanced.
According to the coal and gas outburst, gas explosion hazard prediction and early warning theory, using information fusion method, fully tap the gas, wind speed, electromagnetic radiation, knowledge of the law of acoustic emission and other types of sensing data contains all kinds of sensors, play advantage, "according to a variety of means, complementary advantages, mutual authentication. In the short term collocation" ideas, focus on building a gas safety monitoring and early warning system based on multi sensor information fusion, realize the gas in the working face of the dangerous "real-time perception, accurate identification, rapid response, effective control. The main achievements of this study are as follows:
It analyzes the research results of domestic and foreign dynamic monitoring system of coal mine gas safety and outburst risk evaluation index, including gas concentration, electromagnetic radiation and acoustic emission sensing technology and prediction methods, to play their respective advantages, realize the coal and gas outburst early warning sensor fusion laid a solid theoretical foundation.
Through field investigation, analysis of the mine gas disaster prevention based on the actual demand, improve the efficiency of the monitoring system, reduce the system resource consumption concept, put forward the target gas monitoring system of multi-sensor information fusion, closed-loop workflow analysis, rational use of theory of selection and organization as well as various sensor gas safety monitoring sensor information fusion, and finally the multi sensor information fusion system structure of gas monitoring, focusing on the prediction of multi sensor information decision fusion method of gas outburst based on fuzzy expert system.
The similar gas measurement based on time sequence alarm signal identification method. The DTW distance of gas in coal mining work face alarm time series clustering analysis based on 7 typical time series model is obtained by segmented shape measurement method based on extraction, 15 indicators, selected 5 classification the strong performance index in the establishment of a gas alarm time series characteristics table. This paper presents a gas alarm signal identification algorithm.
The abnormal automatic identification technology of gas in coal mining work face spatio-temporal correlation analysis based on the monitoring data. The qualitative analysis of the working surface along the spatial correlation characteristics of gas migration in asynchronous wind direction; the correlation coefficient calculation method and the gas in the process of asynchronous step involves optimal lag long after pouring in ventilation roadway in volume fractional change with the time and space prediction and inversion formula; statistical analysis of abnormal gas data of 8 kinds of causes of the correlation coefficient change interval; proposed anomaly recognition algorithm of space time correlation analysis based on the monitoring data of the gas in the working face; in order to improve the efficiency of correlation analysis, put forward the underground gas sensor level encoding method can express spatial information.
The working face of the gas safety monitoring problems classified as expert diagnostic category. On the gas monitoring information of the knowledge discovery method, proposed gas time series clustering analysis and knowledge extraction method; according to the design requirements of Fusion Expert System for gas monitoring of multi sensor information decision, proposed gas monitoring knowledge learning algorithm and storage strategy for gas monitoring expert knowledge. An example of gas expert system working face recognition application process based on the causes of overloading.

【學(xué)位授予單位】：中國礦業(yè)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2013
【分類號】：TD712

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