基于PIC16F877矿用复合传感器的研究
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摘要
近年来我国煤矿事故频繁发生,造成重大经济损失和人员伤亡。目前矿用传感器一般只能测量某一个量,而且分布在矿井巷道中不同的地方,因此每个传感器与井下分站之间都需用数据线进行连接。从而造成了资源的浪费(数据线使用太多),并影响了采集数据的准确性和实时性,也为煤矿安全生产造成一定隐患。针对以上问题,本文研制了以复合传感器技术与传统检测技术相结合的矿用复合传感器。
矿用复合传感器的研制分为硬件设计和软件开发两个方面:硬件方面,根据PIC16F877单片机的特点(哈佛结构、RISC与高性价比等)选用其作为核心处理器,完成了检测电路、显示报警电路、RS232通信接口电路等设计;软件方面,采用加权平均递推算法去除采样过程的噪声,采用零点补偿算法补偿检测元件的零点漂移,采用多传感器数据融合算法提高整个系统的检测精度和可靠性。另外,针对复合传感器的多种算法进行了MATlab仿真分析,验证了BP网络的数据融合算法在该系统当中的可行性与可靠性。
本文所研制的矿用复合传感器,实现了不同层次的创新和突破。矿用复合传感器实现了井下多个量的同时测量与数据传输,节省了资源(大大减少了数据线的使用量)、改善了数据传输的准确性、为研究多传感器数据融合技术在矿井中的应用提供了良好的平台;在矿用复合传感器当中使用单传感器位置级数据融合和多传感器BP神经网络数据融合,大大提高了测量数据的精度;矿用复合传感器的标准通信接口,又为数据传输提供了便利。本文设计的矿用复合传感器在矿井生产中的应用,为煤矿生产提供了更加安全的环境。
The coal mine accidents is occurred in China frequently in recent years which costs large mount of economic lost and casualty. However, the sensors for coal measurement applications are unfeasible which single type of signal could be detected and distributed in the mine tunnels separately. Consequently, the data transferring of the underground sub-stations and sensors is only depended upon the large scale of wire connections between them which the resources is much wasted and the precision and real-time ability of data collection are negatively affected as well. Hence, it is crucially necessary to avoid the hidden dangers in coal safety production. A mining composite sensor with the combination of the technologies of composite sensor and traditional detection is designed for the resolution of those serious issues.
The research of the mining composite sensor is introduced in the aspects of hardware and software. On hardware designing, the single chip PIC16F877 is selected as core processor in accordance with the features like Harvard structure, RISC and cost-effective, etc. The detection, alarm display, RS232 communication interface circuits are designed; in the aspect of software, the noise from the sampling process is effectively removed with the algorithm of weighted average of recursive, and the zero drift of detection devices is compensated by the use of zero compensation algorithm; the whole system detection accuracy and reliability is improved by the use of multi-sensor information fusion method. Moreover, simulation analysis based on MATLAB for multi-algorithm in composite sensor utilization, and the feasibility and reliability of the data fusion algorithm of BP neural network applied in the system is proved successfully.
The accomplishments of the mining composite sensor are built on levels of creativeness and breakthrough. First of all, the multi-type data is measured and transmitted synchronously underground that the resources of wire connections and space are saved, the precision of data transmission is improved and more importantly, a functional platform for the mining application of multi-sensor data fusion technology is provided; the data fusion is carried on with the BP algorithm in multi-sensor research. The application of standard communication interface for mining composite sensor is positively available for data transmitting. Hence, it is proved that advanced safely protected mining environment is realized by the design of composite sensors in mining production application.
矿用复合传感器的研制分为硬件设计和软件开发两个方面:硬件方面,根据PIC16F877单片机的特点(哈佛结构、RISC与高性价比等)选用其作为核心处理器,完成了检测电路、显示报警电路、RS232通信接口电路等设计;软件方面,采用加权平均递推算法去除采样过程的噪声,采用零点补偿算法补偿检测元件的零点漂移,采用多传感器数据融合算法提高整个系统的检测精度和可靠性。另外,针对复合传感器的多种算法进行了MATlab仿真分析,验证了BP网络的数据融合算法在该系统当中的可行性与可靠性。
本文所研制的矿用复合传感器,实现了不同层次的创新和突破。矿用复合传感器实现了井下多个量的同时测量与数据传输,节省了资源(大大减少了数据线的使用量)、改善了数据传输的准确性、为研究多传感器数据融合技术在矿井中的应用提供了良好的平台;在矿用复合传感器当中使用单传感器位置级数据融合和多传感器BP神经网络数据融合,大大提高了测量数据的精度;矿用复合传感器的标准通信接口,又为数据传输提供了便利。本文设计的矿用复合传感器在矿井生产中的应用,为煤矿生产提供了更加安全的环境。
The coal mine accidents is occurred in China frequently in recent years which costs large mount of economic lost and casualty. However, the sensors for coal measurement applications are unfeasible which single type of signal could be detected and distributed in the mine tunnels separately. Consequently, the data transferring of the underground sub-stations and sensors is only depended upon the large scale of wire connections between them which the resources is much wasted and the precision and real-time ability of data collection are negatively affected as well. Hence, it is crucially necessary to avoid the hidden dangers in coal safety production. A mining composite sensor with the combination of the technologies of composite sensor and traditional detection is designed for the resolution of those serious issues.
The research of the mining composite sensor is introduced in the aspects of hardware and software. On hardware designing, the single chip PIC16F877 is selected as core processor in accordance with the features like Harvard structure, RISC and cost-effective, etc. The detection, alarm display, RS232 communication interface circuits are designed; in the aspect of software, the noise from the sampling process is effectively removed with the algorithm of weighted average of recursive, and the zero drift of detection devices is compensated by the use of zero compensation algorithm; the whole system detection accuracy and reliability is improved by the use of multi-sensor information fusion method. Moreover, simulation analysis based on MATLAB for multi-algorithm in composite sensor utilization, and the feasibility and reliability of the data fusion algorithm of BP neural network applied in the system is proved successfully.
The accomplishments of the mining composite sensor are built on levels of creativeness and breakthrough. First of all, the multi-type data is measured and transmitted synchronously underground that the resources of wire connections and space are saved, the precision of data transmission is improved and more importantly, a functional platform for the mining application of multi-sensor data fusion technology is provided; the data fusion is carried on with the BP algorithm in multi-sensor research. The application of standard communication interface for mining composite sensor is positively available for data transmitting. Hence, it is proved that advanced safely protected mining environment is realized by the design of composite sensors in mining production application.
引文
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[2]陈杰,黄鸿.传感器与检测技术[M].北京:高等教育出版社:2002
[3]郑华耀.检测技术[M].北京:机械工业出版社,2004
[4]刘君华.智能传感器系统[M].西安:西安电子科技大学出版社,1999
[5]杨宝清.现代传感器技术基础[M].北京:中国铁道出版社,2001
[6]王仲生等.智能监测与控制技术[M].西安:西北工业大学出版社,2002
[7]孙宏军等.智能仪器仪表[M].北京:清华大学出版社,2007
[8]王在英等.过程控制系统与仪表[M].北京:机械工业出版社,2007
[9]易继凯等.智能控制技术[M].北京:北京工业大学出版社,1999
[10]王煜东.传感器应用技术[M].西安:西安电子科技大学出版社,2006
[11]刘君华.智能传感器系统[M].西安:西安电子科技大学出版社,1999
[12]何道清.传感器与传感器技术[M].北京:科学出版社,2003
[13]杨世兴,龚尚福.监测监控系统.[M].西安:西安出版社,1999
[14]靳伟,廖延彪,张志鹏.导波光学传感器原理与技术[M].北京:科学出版社,1998
[15]孙传经.气相色谱分析原理与技术[M].北京:化学工业出版社,1979
[16]朱宗玖,程桦,减东亮.光纤传感器在煤矿井壁安全检测中的应用[J].煤矿机械,2003,No.12:98~99
[17]董永贵.传感技术与系统[M].北京:清华大学出版社,2006
[18]李学诚,李先才.红外线瓦斯传感器在井下的试用[J].煤矿安全,1999,2:10~13
[19]陈娟,冯锡钰,马莹.红外吸收型光纤甲烷气体传感器的研究[J].吉林工学院学报,1997,18(2):57~60
[20]李虹.监测甲烷浓度的红外光吸收法光纤传感器[J].量子电子学报,2002,19(4):355~357
[21]童敏明.催化传感器的研究与应用技术[M].江苏:中国矿业大学出版社,2002
[22]童敏明,杨胜强,田丰.新型瓦斯传感器关键技术的研究[J].中国矿业大学学报,2003,32(4):399~401
[23]王汝琳.矿井瓦斯传感器的近代研究方法及方向[J].中国矿业大学学报,1998,4:16~18
[24]沈玉秀,唐祯安,张洪泉.热线式传感器的研究[J].传感器技术,2004,23(5):15~18
[25]朱良,谭德荣,赵玮.热线式空气流量计的测量技术[J].山东理工大学学报(自然科学版),2006,20(3):81~84
[26]杨杰.微机型超声瞬时风速风谱测量系统[J].合肥工业大学学报(自然科学版),1999,22(6):89~93
[27]盛森芝,沈熊,舒炜.流速测量技术[M].北京大学出版社,1987
[28]孙传友等.测控系统原理与设计[M].北京:北京航空航天大学出版社,2002
[29]徐安等.微控制器原理与应用[M].北京:科学出版社,2006
[30]徐英惠等.ARM9嵌入式系统设计[M].北京:北京航空航天大学出版社,2007
[31]陈启美等.现代数据通信教程[M].南京:南京大学出版社,2006
[32]侯媛彬.系统辨识及其MATLAB[M].北京:科学出版社,2004
[33] Jones , Charles H ,“IEEE1451 . 4 Smart Transducers TemPlate Description Language,”May2004
[34] Peng T M,Hubele N F,Karady G G..Advancement in the Application of NeuralNetworks for Short-term load Forecasting.IEEE Trans.on Power Systems,1992,7(1):250~257
[35]杨行峻,郑君里.人工神经网络[M].北京:高等教育出版社,1992
[36]焦李成.神经网络理论[M].西安:西安电子科技大学出版社,1990
[37] Kennedy M P and Chua L O.Neural Networks for Nonlinear Programming.IEEE Trans,CAS-25(5),1988:554~562
[38]康耀红.数据融合理论与应用[M].西安:西安电子科技大学,1997
[39]侯媛彬,杜京义.王梅.神经网络[M].西安:西安电子科技大学出版社,2007
[40] (美)Mrtin T. Hagan Howard B. Demuth Mark H.Beale著.戴葵等译.神经网络设计[M].北京:机械工业出版社,2005:227~257
[41] Witherspoon,J R.Odour emission estimates using models sampling.Water scicence and technology[J].Vo1.41,No.6,2000
[42] Golnaraghi M F,Jazar G Nakhaie,Narimani .A Sensitivity analysis of the frequency response of a piecewise linear system in a frequency island[J].Journal of Vibration and Control,2004,10(2):175~198
[43]何友,彭应宁等.多传感器数据融合模型综述[J].清华大学学报,1996,(9):16~20
[44]“IEEE Std 1451.4-2004,Standard for a Smart Transducer Interface forSensorsand Actuators-Mixed-ModeCommunication Protoeols and Transducer Electronic Data Sheet (TEDS)Formats”,IEEE Standards Association,Piscataway,NJ,subclause5.1.2,2004
[45]闻新.MATLAB神经网络仿真与应用[M].北京:科学出版社,2003
[46]陈希孺.高等数理统计学[M].合肥:中国科学技术出版社,1991
[47] W.N.Venables.B.D.Ripley Modern Applied Statistics with S-PLU S.Second Edition. Springer.1997
[48]卢智远,周永军,李卫军.传感器非线性误差校正的BP神经网络方法研究[J].传感器技术,2005,24(2):11~12
[49]黄天戌,倪效勇,张红剑等.神经网络在校正传感器非线性度方面的研究[J].传感器技术,2003,22(12):48~50
[50] Ronald R Yager. A framework for multi-source data fusion[J]. Information Sciences,2004,(163):175~200
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