基于声发射监测的矿井突水前兆特征信息获取方法的研究
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摘要
煤矿水害是与瓦斯、顶板等并列的矿山建设与生产过程中的主要安全灾害之一。突水过程具有岩体应力变化、水压升高等前兆特征,并伴随释放声发射(AE)信号,大量的岩体内状态变化信息包含在AE信号中,分析、处理获取的AE信号,并把突水前兆特征信息提取出来,这可作为突水监测预报的依据。这对有效遏制我国重大矿井突水事故的发生具有一定的指导作用。
本文通过理论分析和实验研究等方法,从信号处理的角度,分别研究了基于声发射监测的井下干扰信号的识别、声发射信号的预处理和突水前兆特征信息获取方法。主要工作和创新点如下:
(1)研究了井下环境煤岩AE信号和干扰信号特征及其适用的信号降噪方法。针对煤岩AE信号特点,分析了各种降噪处理方法,提出了采用小波对AE信号进行降噪处理。通过理论分析和实验仿真,参照SNR、RMSE两个指标对降噪效果评价,可知sym8、coif5小波适合于AE信号的降噪,并选取小波6层
分解、rigrsure阈值、sln模式、硬阈值处理,得到的降噪效果较其它情况要好。(2)针对声发射波形分析中的限幅饱和问题,提出了一种形态修复方法。对仿真限幅饱和信号进行了修复验证和效果评价,得出形态修复能够较好的复原限幅饱和信号的结论,为获取完整的信号波形和进一步特征提取有重要价值。
(3)提出了小波特征能谱系数和特征向量的信号特征提取方法,以及基于小波特征向量的神经网络识别方法。通过实践分析,小波特征向量能用较少的参数表示信号特征,可用于识别不同类型的干扰信号和响应信号,并且对于抑制响应信号的不稳定性,抑制同频段的干扰信号也非常有利。基于小波特征向量的神经网络具有结构简单、运行时间短等优点,有利于实现实时的模式识别。
(4)提出利用小波包分析提取不同应力速率下含水和烘干煤岩试验声发射信号的波形特征。从实验结果看,煤岩在不同含水情况和不同应力速率下破裂时信号的声发射事件数、平均幅值和最大幅值的变化特征是比较明显的。
(5)提出了AE信号小波特征编码和状态时间序列的特征提取方法。并以小波特征编码的依据,提出了声发射信号的特征编码方法,从编码方案的有效性和一致性两个方面验证了小波特征编码的可行性。把每一分析得到的特征编码按时间顺序排列,得到信号的状态时间序列。连续的特征编码不仅可以相互区分,而且包含了能量的连续分布特征,使得波形特征更加有序化,为从时间序列层次上分析煤矿突水声发射事件的演变过程打下重要基础。
该论文有图83幅,表14个,参考文献148篇。
The coal mine flood is one of the main security disasters in mine construction and production process, as well as methane gas and roof. Some precursor characteristics, such as rock mass stress variation, water pressure rise, will generate during water inrush accompanied by the release of acoustic emission (AE) signals. A wealth of information of rock internal state is contained in AE signals. The collected AE signals are processed and extracted precursor feature information as a basis for monitoring and forecasting of water inrush. This will give some guidance to curb China's water inrush incidents.
In this paper, through theoretical analysis and experimental research, from signal processing's angle, mine interference signal recognition, AE signal pre-processing and precursor feature acquisition methods for water inrush are studied separately based on AE monitoring. The main work and innovation are as follows:
(1) The characteristics of mine AE and interference signals, as well as the suitable noise reduction methods are studied. Considering the characteristics of mine AE signal, a variety of noise reduction methods are analyzed, and the noise processing to AE signal by wavelet is proposed. Through theoretical analysis and experimental simulation, Evaluating noise reduction effect by using SNR and RMSE, it can be seen that sym8 and coif5 wavelets are suitable, and the de-noising results are better than the other cases by selecting 6-layer wavelet decomposition, rigrsure threshold, sln mode and hard threshold processing.
(2) Aiming at the limiting saturation problem in AE signal, a shape repair method is proposed. Experimental verification and effect appraisal to emulation limiting saturation signal are carried on. It can be drawn the conclusion that the shape repair is a better recovery method to limiting saturated. This method plays an important role in acquiring complete AE waveform and further feature extraction.
(3) Wavelet characteristic power spectrum coefficient and feature vector are proposed for extracting the signal feature. Furthermore, neural network recognition method based on wavelet feature vector is also proposed. Through practice analysis, wavelet feature vector can express the signal’s characteristic by using fewer parameters, can be used to identify different types of interference signals and response signals. Wavelet feature vector is also very beneficial for restraining the instability of the response signal and suppressing interference signals in the same frequency band. Neural network based on wavelet feature vector has merits of simple structure, short running time, and is also advantageous in realizing the real-time pattern recognition.
(4) The extracting method of waveform characteristics of AE signals is proposed by using wavelet packet in experiments of watery and drying coal rock under different stress rates. From the experimental results, the change characteristics of the AE event number, average amplitude and maximum amplitude are quite obvious under different watery situations and different stress rates.
(5) Wavelet feature coding and condition time series characteristics of AE signal are proposed. The basis of wavelet feature coding is elaborated. The method of feature coding is put forward. The feasibility of wavelet feature coding has confirmed from code scheme's availability and consistency. The condition time series is obtained by chronological arranging feature coding, which acquired from each signal analysis. The continuous feature coding cannot only differentiate mutually, and contains continuous distribution characteristics of energy, makes the waveform characteristics more orderly. This will lay an important foundation for the time sequence analysis of AE event's evolution in mine water inrush.
本文通过理论分析和实验研究等方法,从信号处理的角度,分别研究了基于声发射监测的井下干扰信号的识别、声发射信号的预处理和突水前兆特征信息获取方法。主要工作和创新点如下:
(1)研究了井下环境煤岩AE信号和干扰信号特征及其适用的信号降噪方法。针对煤岩AE信号特点,分析了各种降噪处理方法,提出了采用小波对AE信号进行降噪处理。通过理论分析和实验仿真,参照SNR、RMSE两个指标对降噪效果评价,可知sym8、coif5小波适合于AE信号的降噪,并选取小波6层
分解、rigrsure阈值、sln模式、硬阈值处理,得到的降噪效果较其它情况要好。(2)针对声发射波形分析中的限幅饱和问题,提出了一种形态修复方法。对仿真限幅饱和信号进行了修复验证和效果评价,得出形态修复能够较好的复原限幅饱和信号的结论,为获取完整的信号波形和进一步特征提取有重要价值。
(3)提出了小波特征能谱系数和特征向量的信号特征提取方法,以及基于小波特征向量的神经网络识别方法。通过实践分析,小波特征向量能用较少的参数表示信号特征,可用于识别不同类型的干扰信号和响应信号,并且对于抑制响应信号的不稳定性,抑制同频段的干扰信号也非常有利。基于小波特征向量的神经网络具有结构简单、运行时间短等优点,有利于实现实时的模式识别。
(4)提出利用小波包分析提取不同应力速率下含水和烘干煤岩试验声发射信号的波形特征。从实验结果看,煤岩在不同含水情况和不同应力速率下破裂时信号的声发射事件数、平均幅值和最大幅值的变化特征是比较明显的。
(5)提出了AE信号小波特征编码和状态时间序列的特征提取方法。并以小波特征编码的依据,提出了声发射信号的特征编码方法,从编码方案的有效性和一致性两个方面验证了小波特征编码的可行性。把每一分析得到的特征编码按时间顺序排列,得到信号的状态时间序列。连续的特征编码不仅可以相互区分,而且包含了能量的连续分布特征,使得波形特征更加有序化,为从时间序列层次上分析煤矿突水声发射事件的演变过程打下重要基础。
该论文有图83幅,表14个,参考文献148篇。
The coal mine flood is one of the main security disasters in mine construction and production process, as well as methane gas and roof. Some precursor characteristics, such as rock mass stress variation, water pressure rise, will generate during water inrush accompanied by the release of acoustic emission (AE) signals. A wealth of information of rock internal state is contained in AE signals. The collected AE signals are processed and extracted precursor feature information as a basis for monitoring and forecasting of water inrush. This will give some guidance to curb China's water inrush incidents.
In this paper, through theoretical analysis and experimental research, from signal processing's angle, mine interference signal recognition, AE signal pre-processing and precursor feature acquisition methods for water inrush are studied separately based on AE monitoring. The main work and innovation are as follows:
(1) The characteristics of mine AE and interference signals, as well as the suitable noise reduction methods are studied. Considering the characteristics of mine AE signal, a variety of noise reduction methods are analyzed, and the noise processing to AE signal by wavelet is proposed. Through theoretical analysis and experimental simulation, Evaluating noise reduction effect by using SNR and RMSE, it can be seen that sym8 and coif5 wavelets are suitable, and the de-noising results are better than the other cases by selecting 6-layer wavelet decomposition, rigrsure threshold, sln mode and hard threshold processing.
(2) Aiming at the limiting saturation problem in AE signal, a shape repair method is proposed. Experimental verification and effect appraisal to emulation limiting saturation signal are carried on. It can be drawn the conclusion that the shape repair is a better recovery method to limiting saturated. This method plays an important role in acquiring complete AE waveform and further feature extraction.
(3) Wavelet characteristic power spectrum coefficient and feature vector are proposed for extracting the signal feature. Furthermore, neural network recognition method based on wavelet feature vector is also proposed. Through practice analysis, wavelet feature vector can express the signal’s characteristic by using fewer parameters, can be used to identify different types of interference signals and response signals. Wavelet feature vector is also very beneficial for restraining the instability of the response signal and suppressing interference signals in the same frequency band. Neural network based on wavelet feature vector has merits of simple structure, short running time, and is also advantageous in realizing the real-time pattern recognition.
(4) The extracting method of waveform characteristics of AE signals is proposed by using wavelet packet in experiments of watery and drying coal rock under different stress rates. From the experimental results, the change characteristics of the AE event number, average amplitude and maximum amplitude are quite obvious under different watery situations and different stress rates.
(5) Wavelet feature coding and condition time series characteristics of AE signal are proposed. The basis of wavelet feature coding is elaborated. The method of feature coding is put forward. The feasibility of wavelet feature coding has confirmed from code scheme's availability and consistency. The condition time series is obtained by chronological arranging feature coding, which acquired from each signal analysis. The continuous feature coding cannot only differentiate mutually, and contains continuous distribution characteristics of energy, makes the waveform characteristics more orderly. This will lay an important foundation for the time sequence analysis of AE event's evolution in mine water inrush.
引文
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