基于ARM9嵌入式和工业以太网的矿井瓦斯监控系统研究
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摘要
国内先进的瓦斯安全监控系统普遍采用工业以太环网为通信主干网的通信方式,但瓦斯安全监控系统的核心设备井下监控分站却没有以太网接口,而仅提供了RS485接口或CAN总线接口,这两种接口仅能提供最高5 000bps的通信速率,与工业以太网的100Mbps之间存在巨大差距。另外这些井下监控分站也普遍存在通信协议不规范、各厂家设备互不兼容、监控分站人机界面差、显示面板较小等问题。因此,研制基于ARM9嵌入式CPU、具有大屏幕真彩色LCD屏、具有工业以太网接口、具有工作面瓦斯浓度预测功能、兼容其它厂家产品的井下监控分站,并以此井下监控分站为核心,研制完全基于工业以太网的瓦斯监控与瓦斯预测系统就显得十分必要。论文主要工作如下:
(1)提出了基于小波分析及神经网络的工作面瓦斯浓度预测算法。该算法首先利用小波分析强大的降噪滤波能力,对基于时间序列的现场瓦斯数据进行降噪滤波处理,运用神经网络在预测估值方面的优势,采用降噪后数据对BP神经网络进行训练,然后用现场最新数据代入经过训练后的神经网络进行预测,得到预测结果。
(2)以三星公司ARM9嵌入式芯片S3C2410为核心,设计了井下瓦斯监控分站的硬件部分。重点对CPU及存储模块、LCD显示模块、I/O输入输出模块、以太网接口模块、USB接口模块、串口通信模块进行了设计,给出了各模块的电路图,并对电路进行了本安防爆计算。以嵌入式操作系统Win CE 5.0为核心,设计了井下瓦斯监控分站的软件部分。数据采集软件采用了工控组态软件“组态王嵌入式版”编写,井下监控分站中的工作面瓦斯浓度预测软件由Microsoft Embedded Visual C++(EVC)软件编写。工作面瓦斯浓度预测软件后台运行,通过DDE方式得到数据采集软件中的现场各通道瓦斯浓度值,经小波分析降噪滤波及BP神经网络算法预测后,再将瓦斯浓度预测值返回给数据采集软件,供其画面显示。
(3)设计了完全基于工业以太网的瓦斯监控及预测系统的硬件方案和软件方案。硬件方案以带有工业以太网的井下瓦斯监控分站为核心,全部通信干线以工业以太网总线连接,可实现从监控计算机到井下瓦斯监控分站间的工业以太网直接连通,从而将瓦斯监控及预测系统的通信速率提高到100Mbps。软件方案由KingOPCServer数据采集软件以双机冗余方式,将现场各个井下监控分站数据采集进KingHistorian实时/历史工业数据库,其他相关软件都从KingHistorian数据库读取数据,实现各自的显示、处理、控制、打印等功能。在上位监控计算机上运行的上位监控软件采用组态软件KingView6.53编写,瓦斯浓度预测软件由MatLab7.0软件编写,两者间以OPC方式传递数据。
(4)运用基于Fuzzy-FTA分析法对瓦斯监控及预测系统发生事故的原因进行了分析,提出了相应管理对策。
基于ARM9嵌入式和工业以太网的矿井瓦斯监控系统具有工作面瓦斯浓度预测功能,可以对瓦斯浓度超限提供更长的预警时间。由于监控系统通信速率提高到了100Mbps,可以极大提高系统巡检速度与系统规模。另外只需更换井下监控分站及部分通信线路即可对其他瓦斯监控系统升级,具有广泛的应用前景。
The advanced gas safety monitoring and control systems of domestic manufacturers have widely used industrial Ethernet as communication backbone. However, underground sub-station, the core equipment in gas safety monitoring and control system, does not provide the Ethernet interface, and only provides a RS485 interface or CAN bus interface. The maximum transmission rate of these two interfaces is only 5 000bps, and has huge gap between industrial Ethernet whose rate is 100Mbps. In addition, communication protocol of these systems is not standardized, various manufacturers’equipment is not compatible with each other, and man-machine interface of monitor sub-station is poor. Therefore, it is necessary to develop the underground monitoring sub-stations based ARM9 embedded CPU, which are equipped with a large color LCD screen and the Industrial Ethernet interface, and compatible with other manufacturer’s products. It is also necessary to develop the gas monitoring system based entirely on industrial Ethernet, with the underground sub-station being its core. The main contents of this paper are as follows:
(1)It proposed gas prediction algorithm based on wavelet analysis and neural networks. The algorithm has powerful noise filtering ability. It can process the time series data from gas field. Taking advantage of neural networks in predicting and valuating, we can train the BP neural network by using the post noise filtering data. And then the neural network will output the result of prediction after it is trained to predict according to the latest data put into it.
(2)It designed the underground gas monitoring and control station hardware system Based on the ARM9 core of Samsung S3C2410 embedded chips. The system emphasizes on the CPU and memory modules, LCD display module, I/O Input output module, Ethernet interface module, USB interface module and serial module, gives the corresponding circuit diagram, and analyses the intrinsically safe explosion-proof of the circuit. Based on embedded operating system Win CE 5.0 as the core, the research also designed underground gas monitoring and control sub-station software system. Data acquisition software is written by Control Software "KingView Embedded Edition", the face gas prediction software in underground monitoring sub-station is compiled by Microsoft Embedded Visual C++(EVC). The face gas prediction software runs in the background, and gets the field gas concentration value of each channel through DDE from data collection software. After filtering the noise by wavelet analysis and forecasts of BP neural network algorithm, it sends face predicted gas concentration value back to the data acquisition software, and displays it on the screen.
(3)It designed a completely Ethernet-based industrial gas monitoring and prediction system hardware programs and software programs. With the underground gas monitoring sub-station industrial Ethernet hardware being its core, the hardware connects all communication lines by Industrial Ethernet bus based on tree topology. Thus communication rate of gas monitoring system is improved to 100Mbps or more. The data of each underground field monitoring sub-station is collected by the KingOPCServer software with dual-redundant way into KingHistorian that is real-time/historical industrial database. Other related software all read data from the KingHistorian database to achieve their display, processing, control, printing and other functions. Monitoring software running on the upper monitoring computer is written by configuration software "KingView6.53", Gas prediction software is written by the MatLab7.0 software, transmission of data of both between them is carried out by the ways of OPC.
(4)Finally, it analyses the cause of accident of gas monitoring system through an innovative use of Fuzzy-FTA. And the appropriate management measures were proposed. Mine gas monitoring system based on ARM9 CPU and Industrial Ethernet are capable of gas concentration prediction, so it can provide a longer warning time to gas concentration overrun. Because communication speed of monitoring system has been raised to100Mbps, inspection speed and size of the system are greatly improved. Just replacing the underground sub-station and part of communication lines, other gas monitoring system can be upgraded quickly. Therefore, the system has a wide range of applications.
(1)提出了基于小波分析及神经网络的工作面瓦斯浓度预测算法。该算法首先利用小波分析强大的降噪滤波能力,对基于时间序列的现场瓦斯数据进行降噪滤波处理,运用神经网络在预测估值方面的优势,采用降噪后数据对BP神经网络进行训练,然后用现场最新数据代入经过训练后的神经网络进行预测,得到预测结果。
(2)以三星公司ARM9嵌入式芯片S3C2410为核心,设计了井下瓦斯监控分站的硬件部分。重点对CPU及存储模块、LCD显示模块、I/O输入输出模块、以太网接口模块、USB接口模块、串口通信模块进行了设计,给出了各模块的电路图,并对电路进行了本安防爆计算。以嵌入式操作系统Win CE 5.0为核心,设计了井下瓦斯监控分站的软件部分。数据采集软件采用了工控组态软件“组态王嵌入式版”编写,井下监控分站中的工作面瓦斯浓度预测软件由Microsoft Embedded Visual C++(EVC)软件编写。工作面瓦斯浓度预测软件后台运行,通过DDE方式得到数据采集软件中的现场各通道瓦斯浓度值,经小波分析降噪滤波及BP神经网络算法预测后,再将瓦斯浓度预测值返回给数据采集软件,供其画面显示。
(3)设计了完全基于工业以太网的瓦斯监控及预测系统的硬件方案和软件方案。硬件方案以带有工业以太网的井下瓦斯监控分站为核心,全部通信干线以工业以太网总线连接,可实现从监控计算机到井下瓦斯监控分站间的工业以太网直接连通,从而将瓦斯监控及预测系统的通信速率提高到100Mbps。软件方案由KingOPCServer数据采集软件以双机冗余方式,将现场各个井下监控分站数据采集进KingHistorian实时/历史工业数据库,其他相关软件都从KingHistorian数据库读取数据,实现各自的显示、处理、控制、打印等功能。在上位监控计算机上运行的上位监控软件采用组态软件KingView6.53编写,瓦斯浓度预测软件由MatLab7.0软件编写,两者间以OPC方式传递数据。
(4)运用基于Fuzzy-FTA分析法对瓦斯监控及预测系统发生事故的原因进行了分析,提出了相应管理对策。
基于ARM9嵌入式和工业以太网的矿井瓦斯监控系统具有工作面瓦斯浓度预测功能,可以对瓦斯浓度超限提供更长的预警时间。由于监控系统通信速率提高到了100Mbps,可以极大提高系统巡检速度与系统规模。另外只需更换井下监控分站及部分通信线路即可对其他瓦斯监控系统升级,具有广泛的应用前景。
The advanced gas safety monitoring and control systems of domestic manufacturers have widely used industrial Ethernet as communication backbone. However, underground sub-station, the core equipment in gas safety monitoring and control system, does not provide the Ethernet interface, and only provides a RS485 interface or CAN bus interface. The maximum transmission rate of these two interfaces is only 5 000bps, and has huge gap between industrial Ethernet whose rate is 100Mbps. In addition, communication protocol of these systems is not standardized, various manufacturers’equipment is not compatible with each other, and man-machine interface of monitor sub-station is poor. Therefore, it is necessary to develop the underground monitoring sub-stations based ARM9 embedded CPU, which are equipped with a large color LCD screen and the Industrial Ethernet interface, and compatible with other manufacturer’s products. It is also necessary to develop the gas monitoring system based entirely on industrial Ethernet, with the underground sub-station being its core. The main contents of this paper are as follows:
(1)It proposed gas prediction algorithm based on wavelet analysis and neural networks. The algorithm has powerful noise filtering ability. It can process the time series data from gas field. Taking advantage of neural networks in predicting and valuating, we can train the BP neural network by using the post noise filtering data. And then the neural network will output the result of prediction after it is trained to predict according to the latest data put into it.
(2)It designed the underground gas monitoring and control station hardware system Based on the ARM9 core of Samsung S3C2410 embedded chips. The system emphasizes on the CPU and memory modules, LCD display module, I/O Input output module, Ethernet interface module, USB interface module and serial module, gives the corresponding circuit diagram, and analyses the intrinsically safe explosion-proof of the circuit. Based on embedded operating system Win CE 5.0 as the core, the research also designed underground gas monitoring and control sub-station software system. Data acquisition software is written by Control Software "KingView Embedded Edition", the face gas prediction software in underground monitoring sub-station is compiled by Microsoft Embedded Visual C++(EVC). The face gas prediction software runs in the background, and gets the field gas concentration value of each channel through DDE from data collection software. After filtering the noise by wavelet analysis and forecasts of BP neural network algorithm, it sends face predicted gas concentration value back to the data acquisition software, and displays it on the screen.
(3)It designed a completely Ethernet-based industrial gas monitoring and prediction system hardware programs and software programs. With the underground gas monitoring sub-station industrial Ethernet hardware being its core, the hardware connects all communication lines by Industrial Ethernet bus based on tree topology. Thus communication rate of gas monitoring system is improved to 100Mbps or more. The data of each underground field monitoring sub-station is collected by the KingOPCServer software with dual-redundant way into KingHistorian that is real-time/historical industrial database. Other related software all read data from the KingHistorian database to achieve their display, processing, control, printing and other functions. Monitoring software running on the upper monitoring computer is written by configuration software "KingView6.53", Gas prediction software is written by the MatLab7.0 software, transmission of data of both between them is carried out by the ways of OPC.
(4)Finally, it analyses the cause of accident of gas monitoring system through an innovative use of Fuzzy-FTA. And the appropriate management measures were proposed. Mine gas monitoring system based on ARM9 CPU and Industrial Ethernet are capable of gas concentration prediction, so it can provide a longer warning time to gas concentration overrun. Because communication speed of monitoring system has been raised to100Mbps, inspection speed and size of the system are greatly improved. Just replacing the underground sub-station and part of communication lines, other gas monitoring system can be upgraded quickly. Therefore, the system has a wide range of applications.
引文
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