煤矿瓦斯时间序列分析方法与预警应用研究
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摘要
论文以国家自然科学基金重点项目为依托,以淮北矿业集团具体工程项目为实际背景,通过数据挖掘对煤矿瓦斯时间序列进行特征提取和模式识别,实现瓦斯事故预警,对于改善当前我国煤矿安全生产的严峻形势具有重要理论意义和重要实际应用价值。
论文研究的主要内容包括以下方面:煤矿瓦斯时间序列的数据采集与预处理,瓦斯时间序列的平稳性、高斯性、线性检验,基于KPCA/KICA的瓦斯时间序列数据降维与特征提取,基于SVM的瓦斯时间序列模式识别与预警应用。
论文基于淮北矿业集团瓦斯预警系统开发实践,论述了该系统体系结构。提出了能够对瓦斯数据进行优先处理的数据编码方法,并利用C语言数据结构加以表示。采用预测编码和游程编码思想,对安全监控系统瓦斯时间序列数据进行压缩,数据压缩效果显著,压缩后的瓦斯时间序列能够表达原始信息。研究给出了清理后的典型瓦斯时间序列及其波形曲线。基于排队理论提出了有无优先权2种瓦斯数据处理模型,理论分析和实验结果同时表明:对于煤矿瓦斯预警系统采用具有优先权排队系统模型处理数据,平均花费时间约为没有优先权排队系统模型的1/30。
论文将计量经济学领域中的热点研究——单位根检验方法引入到瓦斯时间序列的平稳性检验,分别利用ADF/PP两种方法,对瓦斯时间序列进行检验,结果表明瓦斯时间序列只有在正常情况下是平稳的,在有突出危险、停风、割煤/放炮、瓦斯传感器调校时的瓦斯时间序列则表现为非平稳的。利用能够同时检验时间序列线性、高斯性的Hinich检验算法,基于双谱与双相干系数对瓦斯时间序列进行检验,结果表明瓦斯时间序列在正常、有突出危险、停风、割煤/放炮、瓦斯传感器调校各种情况下,均表现为非高斯性和非线性。
论文研究阐述了核方法的思想框架以及相关数学理论基础。基于集合理论,度量空间理论,算子理论,矩阵理论以及核方法,统一用向量(矩阵)范数表示的距离定义单变量或多变量、线性或非线性时间序列的相似性,并证明了这种定义在原始空间和变换空间是等价的,实现了对时间序列相似性定义的拓展,从理论上进行了统一。研究了基于KPCA/KICA的多变量时间序列降维和特征提取理论与算法,通过Matlab对人工合成数据的实验仿真,论证了KPCA/KICA方法的有效性,它们均优于对应的传统PCA/ICA方法。提出从用于检验瓦斯时间序列非平稳性、非高斯性、非线性的24个统计量参数组成的多变量时间序列MTS中提取瓦斯时间序列信息特征,分别利用KPCA、KICA方法对该MTS进行降维和特征提取。结果表明,KPCA方法只需两个主成份即可非常清晰地表明瓦斯时间序列具有5个不同的类别分布, KICA方法分离的三个独立成分表示这5个类别的特征也非常显著,说明KPCA/ KICA方法在瓦斯时间序列数据降维与特征提取上的有效性。
论文利用最小二乘支持向量(LS-SVM)算法,基于一对多编码方法(One versus All Coding,OneVsAll)、一对一编码方法(One Versus One Coding,OneVsOne)、误差纠错编码方法(Error Correcting Output Code,ECOC)、最小输出编码方法(Minimum Output Coding,MOC)构建多类分类器。对人工合成三螺旋线数据进行分类,结果表明:通过调整高斯径向基核函数的参数γ,2σ2,例如,在γ=10, 2σ2=0.01时,MOC分类器对三螺旋线数据的分类精度能够达到100%。基于KPCA、KICA提取的瓦斯时间序列信息特征,对瓦斯时间序列进行分类,结果表明:通过调整高斯径向基函数的参数γ,2σ2,例如,在γ=1,2σ2=0.3125时,MOC分类器对瓦斯时间序列5个类别的分类精度能够达到100%。
论文还介绍了研究成果在淮北矿业集团瓦斯预警系统中的应用效果。
该论文有图53幅,表16个,参考文献218篇。
By the National Natural Science Foundation of China key projects as support, and the Huaibei Coalmine Group Corp specific project as the actual background, this dissertation extracts the features from gas times series, and completes its pattern recognition, then achieves early-warning of gas accidents by using data mining methods, which has both important theoretical significance and important practical application significance for China coalmines to improve the current grim situation of their safe production.
The main research contents of this dissertation include the following areas: the data acquisition and preprocessing of coalmine gas times series; testing for stationarity, linearity and gaussianity of gas time series in coalmine; data dimension reduction and feature extraction of gas time series based on KPCA/KICA; the pattern recognition and its early-warning application of gas time series based on SVM.
Based on the development practice of gas early-warning system in Huaibei Coalmine Group Corp, this dissertation discourses the system’s structure. A method of data coding, which gives priority to the specific gas data, is put forward, and also is represented by the data structure of C language. Using predictive coding and run-length coding idea, the dissertation presents a remarkable data compression method for gas time series data of safety monitoring system, which is proved that the compressed gas time series can express the information of original gas time series. Some typical gas time series and their wave diagrams after cleaning are studied and exposited. Two methods of gas data processing model with priority/non-priority are brought forward based on queuing theory, the theoretical analysis results and the Matlab simulation experiment results simultaneously show that: the average occupation time for gas data processing by using the queuing system model with priority is nearly 1/30 of that of the model without priority.
This dissertation introduces the unit root test methods which are the hotspot in econometrics into the testing for stationarity of gas time series. The method—ADF or PP is used to test the stationarity of gas time series separately. The results simultaneously show that: gas time series only in the normal gas situation is stationary, while it is non-stationary in the following situations, such as outburst, stopping ventilation, cutting coal/ blasting, and gas sensor calibration etc. The Hinich test algorithm, which can simultaneously test the linearity and the Gaussian of time series based on bispectrum and bicoherence coefficient, is used to test gas time series. The results indicate that gas time series is non-linear and non-Gaussian, no matter the situation is normal, outburst, stopping ventilation, cutting coal/ blasting, or gas sensor calibration.
The dissertation expounds the abstract framework of kernel method and the mathematical foundation concerned. Based on set theory, metric space theory, operator theory, matrix theory and kernel method, time series similarity is uniformly defined by the distance derived from the vector (matrix) norm, no matter time series is univariate or multivariate, and linear or non-linear. It is proved that this definition in original space is equivalent to that in transformed space, which theoretically realizes the continuation and the unification of definition of time series similarity. The theory and the algorithm on data dimension reduction and feature extraction of multivariate time series based on KPCA/KICA is researched, and the mathematical simulation for synthetic data using Matlab is given. The experiments prove that they are all better than respective traditional PCA/ICA. The dissertation firstly puts forward a method to extract the features of gas time series from the multivariate time series (MTS) which is composed by twenty-four statistic parameters used to test for stationarity, linearity and gaussianity of gas time series. The results show that: KPCA only needs two principal components to express very clearly that gas time series has five different classification distributions; the features of this five classifications is also very notably expressed in the three independent components separated by KICA. All of this explain the validity of KPCA/KICA on the data dimension and feature extraction of gas time series.
Based on One Versus All Coding, One Versus One Coding, Error Correcting Output Code, and Minimum Output Coding respectively, the dissertation uses least squares support vector (LS-SVM) binary classifier to construct multi-classifiers. They are used to classify the synthetic three-spiral data. The result shows that: by adjusting the Gaussian RBF kernel function parametersγ,2σ2, for example, whenγ=10, 2σ2=0.01, the classification precision of MOC classifier can achieve 100%. On the other hand, they are used to classify gas time series, based on the extracted features by using KPCA and KICA. The result shows that: by adjusting the Gaussian RBF kernel function parametersγ,2σ2, for example, whenγ=1, 2σ2=0.3125, the classification precision of MOC classifier can also achieve 100%.
The application effects of research achievements are also introduced in gas early-warning system in Huaibei Coalmine Group Corp.
The dissertation has 53 figures, 16 tables and 218 references.
论文研究的主要内容包括以下方面:煤矿瓦斯时间序列的数据采集与预处理,瓦斯时间序列的平稳性、高斯性、线性检验,基于KPCA/KICA的瓦斯时间序列数据降维与特征提取,基于SVM的瓦斯时间序列模式识别与预警应用。
论文基于淮北矿业集团瓦斯预警系统开发实践,论述了该系统体系结构。提出了能够对瓦斯数据进行优先处理的数据编码方法,并利用C语言数据结构加以表示。采用预测编码和游程编码思想,对安全监控系统瓦斯时间序列数据进行压缩,数据压缩效果显著,压缩后的瓦斯时间序列能够表达原始信息。研究给出了清理后的典型瓦斯时间序列及其波形曲线。基于排队理论提出了有无优先权2种瓦斯数据处理模型,理论分析和实验结果同时表明:对于煤矿瓦斯预警系统采用具有优先权排队系统模型处理数据,平均花费时间约为没有优先权排队系统模型的1/30。
论文将计量经济学领域中的热点研究——单位根检验方法引入到瓦斯时间序列的平稳性检验,分别利用ADF/PP两种方法,对瓦斯时间序列进行检验,结果表明瓦斯时间序列只有在正常情况下是平稳的,在有突出危险、停风、割煤/放炮、瓦斯传感器调校时的瓦斯时间序列则表现为非平稳的。利用能够同时检验时间序列线性、高斯性的Hinich检验算法,基于双谱与双相干系数对瓦斯时间序列进行检验,结果表明瓦斯时间序列在正常、有突出危险、停风、割煤/放炮、瓦斯传感器调校各种情况下,均表现为非高斯性和非线性。
论文研究阐述了核方法的思想框架以及相关数学理论基础。基于集合理论,度量空间理论,算子理论,矩阵理论以及核方法,统一用向量(矩阵)范数表示的距离定义单变量或多变量、线性或非线性时间序列的相似性,并证明了这种定义在原始空间和变换空间是等价的,实现了对时间序列相似性定义的拓展,从理论上进行了统一。研究了基于KPCA/KICA的多变量时间序列降维和特征提取理论与算法,通过Matlab对人工合成数据的实验仿真,论证了KPCA/KICA方法的有效性,它们均优于对应的传统PCA/ICA方法。提出从用于检验瓦斯时间序列非平稳性、非高斯性、非线性的24个统计量参数组成的多变量时间序列MTS中提取瓦斯时间序列信息特征,分别利用KPCA、KICA方法对该MTS进行降维和特征提取。结果表明,KPCA方法只需两个主成份即可非常清晰地表明瓦斯时间序列具有5个不同的类别分布, KICA方法分离的三个独立成分表示这5个类别的特征也非常显著,说明KPCA/ KICA方法在瓦斯时间序列数据降维与特征提取上的有效性。
论文利用最小二乘支持向量(LS-SVM)算法,基于一对多编码方法(One versus All Coding,OneVsAll)、一对一编码方法(One Versus One Coding,OneVsOne)、误差纠错编码方法(Error Correcting Output Code,ECOC)、最小输出编码方法(Minimum Output Coding,MOC)构建多类分类器。对人工合成三螺旋线数据进行分类,结果表明:通过调整高斯径向基核函数的参数γ,2σ2,例如,在γ=10, 2σ2=0.01时,MOC分类器对三螺旋线数据的分类精度能够达到100%。基于KPCA、KICA提取的瓦斯时间序列信息特征,对瓦斯时间序列进行分类,结果表明:通过调整高斯径向基函数的参数γ,2σ2,例如,在γ=1,2σ2=0.3125时,MOC分类器对瓦斯时间序列5个类别的分类精度能够达到100%。
论文还介绍了研究成果在淮北矿业集团瓦斯预警系统中的应用效果。
该论文有图53幅,表16个,参考文献218篇。
By the National Natural Science Foundation of China key projects as support, and the Huaibei Coalmine Group Corp specific project as the actual background, this dissertation extracts the features from gas times series, and completes its pattern recognition, then achieves early-warning of gas accidents by using data mining methods, which has both important theoretical significance and important practical application significance for China coalmines to improve the current grim situation of their safe production.
The main research contents of this dissertation include the following areas: the data acquisition and preprocessing of coalmine gas times series; testing for stationarity, linearity and gaussianity of gas time series in coalmine; data dimension reduction and feature extraction of gas time series based on KPCA/KICA; the pattern recognition and its early-warning application of gas time series based on SVM.
Based on the development practice of gas early-warning system in Huaibei Coalmine Group Corp, this dissertation discourses the system’s structure. A method of data coding, which gives priority to the specific gas data, is put forward, and also is represented by the data structure of C language. Using predictive coding and run-length coding idea, the dissertation presents a remarkable data compression method for gas time series data of safety monitoring system, which is proved that the compressed gas time series can express the information of original gas time series. Some typical gas time series and their wave diagrams after cleaning are studied and exposited. Two methods of gas data processing model with priority/non-priority are brought forward based on queuing theory, the theoretical analysis results and the Matlab simulation experiment results simultaneously show that: the average occupation time for gas data processing by using the queuing system model with priority is nearly 1/30 of that of the model without priority.
This dissertation introduces the unit root test methods which are the hotspot in econometrics into the testing for stationarity of gas time series. The method—ADF or PP is used to test the stationarity of gas time series separately. The results simultaneously show that: gas time series only in the normal gas situation is stationary, while it is non-stationary in the following situations, such as outburst, stopping ventilation, cutting coal/ blasting, and gas sensor calibration etc. The Hinich test algorithm, which can simultaneously test the linearity and the Gaussian of time series based on bispectrum and bicoherence coefficient, is used to test gas time series. The results indicate that gas time series is non-linear and non-Gaussian, no matter the situation is normal, outburst, stopping ventilation, cutting coal/ blasting, or gas sensor calibration.
The dissertation expounds the abstract framework of kernel method and the mathematical foundation concerned. Based on set theory, metric space theory, operator theory, matrix theory and kernel method, time series similarity is uniformly defined by the distance derived from the vector (matrix) norm, no matter time series is univariate or multivariate, and linear or non-linear. It is proved that this definition in original space is equivalent to that in transformed space, which theoretically realizes the continuation and the unification of definition of time series similarity. The theory and the algorithm on data dimension reduction and feature extraction of multivariate time series based on KPCA/KICA is researched, and the mathematical simulation for synthetic data using Matlab is given. The experiments prove that they are all better than respective traditional PCA/ICA. The dissertation firstly puts forward a method to extract the features of gas time series from the multivariate time series (MTS) which is composed by twenty-four statistic parameters used to test for stationarity, linearity and gaussianity of gas time series. The results show that: KPCA only needs two principal components to express very clearly that gas time series has five different classification distributions; the features of this five classifications is also very notably expressed in the three independent components separated by KICA. All of this explain the validity of KPCA/KICA on the data dimension and feature extraction of gas time series.
Based on One Versus All Coding, One Versus One Coding, Error Correcting Output Code, and Minimum Output Coding respectively, the dissertation uses least squares support vector (LS-SVM) binary classifier to construct multi-classifiers. They are used to classify the synthetic three-spiral data. The result shows that: by adjusting the Gaussian RBF kernel function parametersγ,2σ2, for example, whenγ=10, 2σ2=0.01, the classification precision of MOC classifier can achieve 100%. On the other hand, they are used to classify gas time series, based on the extracted features by using KPCA and KICA. The result shows that: by adjusting the Gaussian RBF kernel function parametersγ,2σ2, for example, whenγ=1, 2σ2=0.3125, the classification precision of MOC classifier can also achieve 100%.
The application effects of research achievements are also introduced in gas early-warning system in Huaibei Coalmine Group Corp.
The dissertation has 53 figures, 16 tables and 218 references.
引文
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