摘要
电涡流传感器检测技术是近年来应用较广泛的一种无损检测方法,它同磁粉检测、超声波检测、射线检测和渗透检测统称为五大无损检测技术。论文中,首先介绍了电涡流检测技术的现状、应用范围及发展方向,接着阐述了电涡流传感技术在课题中的应用。
深刻理解涡流检测的基本原理,是选择涡流检测方案的理论依据。论文第二章从多个角度分析了电涡流检测的原理,在对电涡流检测等效电路分析的基础上,提出了电涡流检测技术在铝箔厚度在线检测系统中应用的可行性,文中还详细讨论了电涡流传感器的设计方法及课题中电涡流传感器参数地选择。
硬件电路是系统设计的基础,也是整个检测系统功能实现的关键。论文第三章结合铝箔生产现场的实际情况,选取TI公司的DSP微处理器芯片构成硬件电路的微处理系统处理检测到的信号。硬件电路的设计主要分三大部分来实现:激励源电路部分,由分频电路和频率合成电路组成,产生频率稳定的激励信号以确保检测任务的正常进行;传感器变换电路部分,由放大电路、滤波电路、检波电路、鉴相电路和数据采集电路组成,主要将电涡流传感器检测线圈检测到的信号变换成只含有被测信息的离散信号,易于后续电路处理;由DSP芯片构成的微处理系统,主要完成检测系统的数据拟合、显示及与主机通信等功能。本章最后部分介绍了硬件设计过程中常见的干扰问题及课题中采取的消除干扰的措施。
测控系统的硬件电路确定之后,测控系统的主要功能将依赖于软件来实现。论文第四章介绍了课题的软件设计思路以及设计过程中的系统自检、容错处理、软件
抗干扰等问题。
检测过程中不可避免地存在着测量误差,除了从硬件方面考虑减少或消除误差
采取的措施外,论文第五章从数据拟合方面详细介绍了减少拟合误差的方法——免
疫算法,并将数据拟会结果‘SB-P算法对数据的拟合结果进行比较。证明了免疫算
法对数据拟合具有较高的精度。最后一章通过对系统总体误差的分析,结果论证了
课题中检测系统设计方案的可行性。
Eddy-current sensor test technology is a kind of very popular untouched nondestructive test technology developed in last decades, which makes up five major modern nondestructive test technologies together with magnetic particle test, radial test, ultrasonic test and osmosis test. The nowadays of eddy-current test technology, the application field of eddy-current test technology and the development direction of eddy-current test technology are present in this paper.
Understanding the basic theory of eddy-current test is the basis of selecting eddy-current test plan. In chapter 2, eddy-current test theory is analyzed from several aspects. Though analyzed the equivalent circuit of eddy-current test, the reliability of eddy-current test technology on aluminum foil thickness test on line is given and the eddy-current sensor design method and the eddy-current sensor parameters selecting are discussed.
Hardware design is the basis of the whole system and the other system designs must be made on the basis of it. Together with the facts in chapter 3, the hardware microprocessor system that made by DSP chip of TI corporation is used to process the sample signal. Hardware circuit design is divided three parts. Generated frequency circuit includes frequency divided circuit and frequency composed circuit to generate stable frequency signal to sure the test task. Eddy-current sensor conversion circuit consist amplification circuit, band-pass filter circuit, demodulation circuit, differentiation phase and data sampling circuit. These circuits are used to convert the test signal of eddy-current sensor to discrete signal tend to process. The microprocessor system that formed of DSP chip is used to data fitting of test system, data displaying and data communicating with personal computer, etc. The interference questions of hardware design and the measure of
eliminating interference signal in the subject are introduced in the last of this chapter.
After the hardware circuit of test system is conformed, the function of test system is relayed on the software. The software design ideas of the subject, system automatic test during the design, system consistent error disposal and software anti-interference questions are recommended in chapter 4.
There have not avoided test errors in the test process. Except the hardware measures are taken to eliminate the errors, the methods of eliminating errors from data fitting aspect are introduced in detail in chapter 5, that is immune algorithm. The results have testified to get higher precision using immune algorithm to process sampling data compare with using B-P algorithm. Though analyzed the whole error of the test system, the results proved feasibility of the design plan of test system.
引文
[1] 任吉林.涡流检测技术近20年的进展.无损检测,20(5),1998,121-125
[2] 邵泽波.无损检测技术.北京:化学工业出版社,2003.
[3] 汤晓君.气缸内壁激光热处理条纹宽度与厚度的检测.西安理工大学硕士学位论文,2001.
[4] 裴晓梅.基于 LabWindows/CVI 的涡流检测虚拟仪器系统的研究.西安理工大学硕士学位论文,2003.
[5] 单成祥.传感器的理论与设计基础及其应用.北京:国防工业出版社,1999.
[6] 郁有文,常健.传感器原理及工程应用.西安:西安电子科技大学出版社,2000.
[7] 张靖,刘少强.检测技术与系统设计.北京:中国电力出版社,2001.
[8] 张君华.智能传感器系统.西安:西安电子科技大学出版社,1999.
[9] 陈健,林俊明,等.DSP 技术在涡流信号处理中的应用.无损探伤,5,2002,31-33.
[10] 孙传友,孙晓斌,等.测控系统原理与设计.北京:北京航空航天大学出版社,2002.
[11] 张雄伟,陈亮,等.DSP 芯片的原理与开发应用(第三版).北京:电子工业出版社,2003.
[12] 中国计量出版社组编.通用模拟电路.北京:中国计量出版社,2001.
[13] 胡广书.数字信号处理——理论、算法与实现.北京:清华大学出版社,1997.
[14] 李哲英,骆丽,等.DSP基本理论与应用技术.北京:北京航空航天大学出版社,2002.
[15] 戴明桢,周建江.TMS320C54xDSP结构、原理及应用.北京:北京航空航天大学出版社,2001.
[16] 郑红,吴冠.TMS32OC54xDSP 应用系统设计.北京:北京航空航天大学出版社,2002.
[17] 清源科技编.TMS32OC54xDSP 硬件开发教程.北京:机械工业出版社,2003.
[18] TMS32OC54x DSP Reference Set Volume 1: CPU and Peripherals. Texas Instruments, 2001.
[19] TMS320VC5402 Fixed-point Digital Signal Processor Data Manual, Texas Instruments, 2000.
[20] Connecting TMS320C54x DSP with Flash Memory, Texas Instruments, 1999.
[21] 徐科军,陈荣宝,等.自动检测和仪表中的共性技术.北京:清华大学出版社,2000.
[22] 陈润泰,许琨.检测技术与智能仪表.中南工业大学出版社,1999.
[23] 刘益成.TMS320C54xDSP 应用程序设计与开发.北京:北京航空航天大学出版社,2002.
[24] 李忠,杨家玮.深入DSP片内外设的编程.单片机与嵌入式系统应用,7,2002,5-8.
[25] 邓勇,施文康.定点DSP应用系统的低功耗设计.微处理机,2,2001,38-40.
[26] 韩康榕.DSP 系统中的外部存储器设计.电气自动化,3,2002,42-44.
[27] 韩峰,侯亚峰.两类DSP芯片的引导过程分析.电子技术应用,10,2002,65-67.
[28] 王进军,宋道元,等.HPI 自举在 TMS320VC5402 芯片上的实现.电子技术应用,11,2002,61-66.
[29] 肖广安.一种软件去除键抖动的方法.单片机与嵌入式系统应用,7,2001,63-65.
[30] TMS320C54x Assembly Language Tool User's Guide, Texas
Instruments, 2002.
[31] Boot-loading the TMS32OVC54O2 in HPI mode, Texas Instruments, 2002.
[32] 王旭,王宏,等.人工神经元网络原理与应用.沈阳:东北大学出版社,2000。
[33] 楼顺天,施阳.基于MATLAB的系统分析与设计——神经网络.西安:西安电子科技大学出版社,2000.
[34] 葛红.免疫算法综述.华南师范大学学报(自然科学版),3,2002,120-126.
[35] 胡朝阳,文福拴译.免疫算法与其它模拟进化算法的比较研究.电力情报,1998,61-63.
[36] 任平.遗传算法(综述).工程数学学报,16(1),1999,1-8,
[37] 谢云.模拟退火算法综述.微机算机信息,14(5),1998,66-68.
[38] 王磊.潘进,等,免疫算法,电子学报,28(7),2000,74-78
[39] 王磊.免疫进化计算理论及应用.西安电子科技大学博士学位论文,2001
[40] G. Rudolph. Convergence Analysis of Canonical Genetic Algorithms. IEEE Trans.on Neural Network, Vol:5, Iss:1,1994:96-101.
[41] H. Bersini and F.J.Varela. Hints for Adaptive Problem Solving Gleaned from Immune Networks. Proceeding of the First Workshop on Parallel Problem Solving from Nature, Springer & Verlang, 1990.
[42] S. A. Hofmeyr, S. Forrest. Architecture for an artificial immune system. Evolutionary Computation, Vol:8, Iss:4, 2000: 443-47.
[43] 周明,孙树栋.遗传算法原理及应用发表评论,国防工业出版社,1999.
[44] 邵学广,陈宗海,等.一种新型的信号拟合方法——免疫算法.分析化学研究报告,28(2),2000,152-155.
[45] 曹先彬,刘克胜,等.基于免疫进化规划的多层前馈网络设计.软件
学报,11(10),1999,1180-1184.
[46] 吕岗,谭得健,等.基于免疫算法的前馈神经网络权值设计.计算机工程与应用,17,2002,31-32.
[47] 吴志远,邵惠鹤,等.一种新的自适应遗传算法及其在多峰值函数优化中的应用.控制理论与应用,16(1),1999,127-129.69
[48] Dasgupta D. Artificial Immune Systems and Their Applications. Berlin Heidelberg,Springer-Verlang, 1999.
[49] 王凌.智能优化算法及其应用.北京:清华大学出版社,2001.