摘要
随着我国煤炭工业数字化进程的推进，安全生产监测的范围不断扩大，各项技术指标的要求越来越严格。微震、超声波、雷达、声纳、红外、光纤等各种技术系统陆续引入到煤矿生产的实时监测过程中。生产现场采集到的各类数据是确保煤矿安全生产的有力保障。本文立足于煤矿生产监测数据的校正方法研究，涉及到监测设备数据获取单元的模型建立、准确测试、自检以及校正方法研究，目的是保证煤矿安全生产监测设备的性能，确保其获取精准的监测数据，利于煤炭生产安全。
煤矿安全生产监测设备种类繁多，数据获取单元的模型各不相同。衡量其性能的指标有多种，比较关键的有：采集精度、采样速率、有效位数、输入通频带等。这里通过对矿用几类典型设备的数据获取单元进行分析，建立了其前向通道数据模型以及几种不同类型数据获取单元的模型，提出了采用积分非线性作为关键技术指标来同时反映静态和动态性能，并推导了其与其它参数关系的表达式，给出仿真测试结果。
针对煤炭安全生产监测系统对及时、准确数据流的要求，在对数据获取单元的静态与动态测试方法进行分析的基础上，提出了改进的加权最小二乘法动态曲线的拟合测试方法，推导出了直方图测试方法中对采样频率、采样数据量进行约束的公式，利用Cramér–Rao对测试信号的有效性进行了估计。
针对煤矿安全生产监测设备需要自检功能的问题，提出一种适合煤矿监测设备短时自检方法，即基于非线性的斜坡码时测试方法，通过对斜坡信号的非线性解析，推导出煤矿生产监测系统的开机自检算法公式，误差分析可知，输入信号的非线性项可以被估计出来，仿真及实测数据表明该方法所需测试数据占据存储空间小，测试时间短，但有与直方图测试方法相当的测试精度，能实现煤矿监测设备的自检功能。
针对煤矿监测设备数据获取单元通道中存在的大量误差，极大的限制了系统检测的精度问题，对Dither信号的特性展开分析，挖掘出其对煤矿生产监测设备数据获取单元的动态和静态性能具有补偿作用，在此基础上提出了基于Dither的数据校正方法，并设计出复杂的数据获取系统性能校正框架。通过将Dither校正方法与Volterra级数校正方法、纠正矩阵校正方法等从校正时间、校正算法的综合对比，表明该方法具有易于实现的特性，适合煤矿生产监测设备在线或离线校正，并具有较好的测试精度。
在对目前存在的几种不同校正方式的优缺点分析的基础上，将矿山监测设备数据获取单元的非线性误差拓展到二维空间，提出了基于Dither技术的非线性纠正矩阵数据校正（Dither Combined with Correction Nonlinear Matrix：DCNM）方法。讨论了构成纠正矩阵的三个组成部分对纠正矩阵构成的影响、纠正矩阵校正的限制条件，验证了二维纠正矩阵校正的可行性，进而建立了校正有效性的评价方法；仿真实验结果表明DCNM方法可以有效的改善系统的性能。
最后设计并制作了可用于地矿勘探的雷达数据获取高速采集硬件，其不但支持一般的采集任务，还能够完成自检、校正功能；然后采用本文提出的DCNM方法，分别对标准的高频信号、掘进爆破信号进行测试与跟踪，并与传统测试方法进行了比较，结果表明，本文提出的DCNM方法具有测试时间短、校正数据精准度高的特点，该校正方法可以推广到煤矿监测的其它设备应用。
With the promotion of China's coal industry digitization and the expansion of productionprocess safety monitoring range in-depth, many detection techniques are gradually introducedinto the process of real-time monitoring in mine production, such as microseisms, ultrasonic,radar, sonar, infrared, optical fiber. Various types of data gathering from production site is astrong guarantee to ensure mine safety production. The correction method of mineproduction’s accurate monitoring data is studied in this paper, including modeling,measurement, self-testing and and error compensation of the mine equipment’s dataacquisition part, and the purpose is to ensure the performance of coal mine production safetymonitoring equipment, to access to accurate monitoring data, help the safe coal production.
There is a wide range of monitoring devices for coal mine safety production, whichinvolving a considerable number of sensor types and different data acquisition unit models.There are several indicators to measure its performance, the key is sampling accuracy,sampling rate, effective number of bits, input passband etc. The nonlinear error of the generaldata acquisition unit is caused by both static and dynamic aspects of distortion, therefore it’snecessary to weigh test methods carefully. For the purpose of determining the key parameters,there analyzed the obtained data of several types of typical devices in mine, established aforward channel data model and several different types of core models. Integral nonlinearityas its key performance indicator is proposed, and the expression of relationship among otherparameters is derived, with the given simulation test results.
Aiming at the data requirements of timeliness and accuracy in coal safety productionmonitoring system, based on the analysis of static and dynamic testing methods in the dataacquisition unit, an improved weighted least squares dynamic curve fitting method isproposed. Data selection formula of sampling frequency and amount of sampling data arederived in histogram test methods, and the validity of test signal is estimated using the Cramér–Rao bounds.
Aiming at the problem of self-test function of mine safety production monitoring devices,a short-term self-test method which is suitable for coal mine monitoring devices is proposed,which is the test method of the ramp test signal code. According to nonlinear analysis of theramp signal, the algorithm formula of power-on-self-test in mine production monitoringsystem is derived. The error analysis shows that nonlinear terms of the input signal can beestimated. Simulation and measured data show that the required test data in this methodaccount for a small storage space and need short test time. But test accuracy is equivalent tothe histogram test method and able to achieve self-test for the coal mine monitoring devices.
Aiming at the problem of too large errors existing in data acquisition unit channel ofmine monitoring devices which greatly limits the accuracy of system detect, the dithersignal’s characteristics are analyzed, which digged out that it has compensation to thedynamic and static performance of data acquisition unit in mine production monitoringdevices. On this basis, a data correction method based on Dither is proposed and aperformance calibration framework of complex data acquisition system is designed.Comparing Dither correction method with Volterra series correction method and correctivematrix correction method from aspects of calibration time and correction algorithm show thatthis method is easy to implement and fit for online or offline correction of mine productionmonitoring devices with good test accuracy.
Based on the analysis of the advantages and disadvantages of several different correctionmethods currently exist, the nonlinear error of data acquisition unit in mine monitoringdevices are expanded to two-dimensional space. A dither combined with correction nonlinearmatrix method is proposed. The impact of three components which constitute a correctivenonlinear matrix, the constitute of corrective matrix and restrictions of corrective matrixcorrection are discussed. The feasibility of two-dimensional corrective matrix correction areverified. Thereby, the evaluation method of correction validity is established. The simulationresults show that the DCNM method can effectively improve the performance of the system.
At last, high-speed acquisition hardware for obtaining radar data which can be used forgeology and mineral exploration is designed and made. It not only supports the generalacquisition task, but also can be able to complete self-test and correction function. Then theDCNM method is used to test and track the standard high-frequency signal, blasting signal.The method is compared with the traditional test methods, which show that the DCNMmethod has the characteristics of short test time and high accuracy of correction data. Thiscorrection method can be extended the application to other coal mine monitoring devices.
引文
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