机械设计制造及其自动化专业毕业设计(论文)外文资料翻译

1、淮 海 工 学 院毕业设计(论文)外文资料翻译系 （院）： 机 械 工 程 专业班级： 机械设计制造及其自动化 机械038 姓 名： 学 号： 外文出处： 附 件： 1.外文资料翻译译文；2.外文原文。 指导教师评语： 签名： 年 月 日针对真空树脂灌注机镜头设备建立在自动诊断系统上的模糊数学何政文 许钰 吴俊西安交通大学管理学院，中国西安710049摘要：在分析真空树脂镜头药设备的基础之上，模糊数学模型已经建立起来，并且模糊关系矩阵，症状模糊向量，模糊复合运算操作，模型的诊断原则已经确定。接着，设备的错误自动诊断系统也被设计完成，实时监控状态的功能和故障自动诊断可以得以实现。最后，故障自动诊

2、断在实际生产下完成实验并且系统的真实性得到验证。关键词：模糊模型，故障自动检测系统，真空树脂灌注机设备1引言真空树脂灌注机设备（vrsde）特别主要是用于各种电器元件的灌注，比如汽车、摩托车的打绕组，变压器，传感器，电容器等配有环氧树脂的器件（一下简称“工件”）。它的作用首先是净化和把环氧树脂（液态甲）和固态催化剂（液态乙）在真空下合成，接着把他们按照精确的比例和量注射到工件中，这样就完成了环形树脂的注射。基本上，和大多数复杂的设备一样，vrsde的错误信息环境是模糊的。从分析错误和他们起因的关系开始，我们可以建立错误和起因之间的数学模型，同样我们可以在模糊原理的基础上对vrsde建立数学模型

3、 。在这个模型基础上我们建立了设备的故障自动检测系统已达到实时监控运行状态和设备故障诊断的功能.系统的真实性在实际操作中得到验证.2主要的错误和他们的起因根据注射质量和过程的影响,vrsde的主要错误可以归结成以下几种:比列的浮动,不充分混合,不够净化,注射口的泄露,量子的不稳定,量子的减少和没有液体从射击口出来.我们在对错误形成机理的研究表明在不同程度上错误的发生和以下的原因有关: 单方向阀处于故障状态中，关阀处于故障状态中，注射器喷洒时液体的泄露，注射器内的液体渗漏，用于净化的容器内的液位或真空过低，液体温度的不稳定，缸向下移动的速度过低，气压过低，计量泵磨损严重，测量系统的机器精度过低，

4、比例过大或过小并且树脂含有石英。错误及其原因的相关程度可以定性的描述成表1。表1 vrsde的主要错误及其原因的相对程度比例浮动不充分混合不精确净化喷射嘴的泄露量子的波动量子减少喷射嘴中没有液体喷出单方向阀处于故障状态中/关阀处于故障状态中/喷射厅中的液体泄露/注射器内的液体渗漏/用于净化的容器内的液位过低/用于净化的容器内的真空过低/液体温度的不稳定/缸向下运动的速度太低/气体压力太低/计量泵磨损严重/测量系统的机器精度过低/比例过大或过小/树脂含有石英/说明：“”的数量表示错误及其原因的相对度3错误诊断的模糊模型的结构我们确定了vrsde运行状态中的各种故障，用v来发表： v1 比例浮动

5、v2 不充分混合v3 不够净化v = v4 = 喷射嘴泄露v5 量子浮动 v6 量子减少v7 没有液体从喷射嘴喷出 而引起各种错误症状的原因，我们用u表示： u1 单方向阀故障状态 u2 闭阀故障状态 u3 喷射厅的液体泄露 u4 注射器内的液体泄露 u5 用于净化的容器内的液位过低 u6 用于净化的容器内的真空过低 u = u7 = 液体温度不稳定 u8 缸向下运动的速度太低 u9 气体压力太低 u10 计量泵磨损严重 u11 测量系统的机器精度过低 u12 比例过大或过小 u13 树脂含有石英运行状态u和症状v之间的模糊关系可以用矩阵r来表示: r11 r12 . r17 r = r21

6、r22 . r27 . . . . r13.1 r13.2 r13.4当rij=uji, 0rij1, 1i13 , 1j7 时, rij 定义为vrsde的运行状态j和症状i的相对度.通过对传感器数据的处理,我们可以得到症状模糊矩阵:a:a=a1/u1+a2/u2+.+a13/u13=(a1,a2,.,a13) 0ai1 i=1,2,.,13a是症状u的一个子集,并且它的元素ai代表u到集合a的映射.元素ai表示症状ui在总的症状中的有效程度,或者从某种意义上讲代表原因i严重与否.在模糊矩阵a和模糊关系矩阵r得到之后,状态模糊矩阵b可以从下面的公式算得. b=a*r“*”代表模糊复合运算.矩

7、阵b是症状v的一个子集,可由下列公式运算得到:b=b1/v1+b2/v2+.+b7/v7=(b1,b2,.b7)公式中0 =bja4矩阵a中,表示在注射厅内泄露的元素在注射厅内的真空度和和管子接头处液体压力相差太大(p)down-half-t 元素数值1 pa4=1-e-k(p-) pia6矩阵a中, 表示用于净化的容器内的真空度太低的元素用于净化的容器内的真空度(pi)up-half-gauss 元素数值 a6=max(a6i)0 pia6i=1-e-k(pi-100)2 pia8矩阵a中, 表示缸向下运动的速度太低的元素缸向下运动的速度(x)down-half-trapezia 元素数值1

8、 0xa8= (r2-x)/(r2-r1)12a9矩阵a中, 表示气体压力过低的元素气体压力(p)down-half-gauss 元素数值1 pa9=e-k(p-5)2 p表3 症状模糊矩阵a中定义域不是在实数范围的元素的确定元素解释元素的确定法则a1矩阵a中,表示单方向阀处于故障状态的元素单方向阀的运动精度&0.05mm&0.05mma1=0.98a1=0.00两个单方向罚的运动情况t0.5s0.1s0.05mma2=0.96&0.05mma2=0.00闭阀关闭时的压力p4.8ca7=0.952.6c3.6ca7=0.931.9ctb4.6ca7=0.982.1ctmix4.6ca7=0.6

9、5tmix2.1ca7=0.00a10矩阵a中,表示计量泵磨损严重的元素树脂含有石英a10=0.15树脂不含有石英a10=0.00a11矩阵a中,表示测量系统的机器精度过低的元素在2年内vrsde的操作状态a11=0.08在2-4年vrsde的操作状态a11=0.13超过4年vrsde的操作状态a11=0.25a12矩阵a中,表示比例过大或过小的元素a:b=100:100a12=0.08a:b=100:80a12=0.09a:b=100:34a12=0.34a13矩阵a中,表示树脂中含有石英的元素树脂含有石英a13=0.80树脂不含有石英a13=0.0034错误诊断原则的确定为了准确的鉴定的v

10、rsde操作状态并且提供和错误有关的足够多的信息,我们定义了两个值1和2 (12),并把它的工作状态归结为以下三类:正常状态,提前警告状态和故障状态.错误模糊诊断的原则可以描述成: 当max (bj) 1时, vrsde处于正常状态当1 2时.vrsde处于故障状态当设备处于提前警告状态或故障状态时,会发出预警信号或警告信号来警告操作同时系统会提供错误及其相关信息.实验表明1 和2的最大值如下: 1 =0.50 2 =0.804错误自动诊断实验在建立错误诊断的数学模型之后,我们利用诸如像传感器这样的硬件,数据采集器,电脑,报警线路和相关的软件建立了vrsde的错误自动诊断系统,这样就实现了对设

11、备操作条件,错误诊断,相关错误的监控并且显示出相关的信息等.错误自动诊断系统的数学模型建立在无数次实验得来的经验的基础上,因此我们必须进行实验来证实其准确性.实验的方法可以描述成如下:通过对设备实际操作,如果系统给出一个警告并且暗示设备存在错误,我们可以进行特殊的试验来检测错误存在与否,这样可以保证系统准确无误.此外,我们可以有周期的检查vrsde的工作参数来确定设备是否存在自身不能识别出来的错误.在2000年5月到2000年11月的半年时间里，我们在西北林业机械股份有限公司针对vcd-m3 vrsde进行了一系列实验，从中得到了表4的数据。从这些数据中我们可以看出诊断的正确率达到了93.3%

12、，系统的设计也基本达到要求。那么错误诊断的数学模型同样可以间接从这结果中得到证实。在表格中可以看出在2000年月31日有一个错误诊断。这一错误的原因是控制闭阀位置的活塞震荡过，从而活塞被固定住，不能动弹。这种现象发生的可能性极小，甚至可以称为偶然。除了上述事实，我们仍然每周测试一次vrsde的工作参数并且在这段时间内没有发现系统自身检测不了的问题.表4 错误自动诊断实验的数据警告日期操作状态的相关信息原因(ai)的相关信息诊断的准确性状态模糊矩阵b错误信息5月11日(0.90 0.46 0.18 0.310.85 0.25 0.34)比例不稳定量子浮动钢球在单方向阀a中被液体淹没(a1 =0.

13、98)准确5月26日0.93 0.42 0.96 0.680.92 0.95 0.36不充分混合,量子减少,比例减少,量子浮动用于净化的容器a液位过低 (a5=1.00)准确6月10日0.81 0.41 0.18 0.330.72 0.29 0.36比例不稳定在两个单方向阀中有运动时间差(a1=0.85)准确6月15日0.23 0.42 0.20 0.810.73 0.34 0.36喷射嘴泄露在喷射厅内有液体泄露(a4=0.91)准确6月27日0.32 0.46 0.90 0.650.66 0.44 0.37不够纯净用于纯净的容器内的真空度过低 (a6=0,94)准确7月6日0.23 0.42

14、 0.20 0.910.91 0.94 0.78 0.56喷射嘴泄露量子浮动闭阀存在一个运动错误 (a2=0.96)准确7月13日0.24 0.86 0.18 0.320.36 0.28 0.37不充分混合缸的运动速度过低(a8=0.93)准确7月28日0.42 0.41 0.91 0.630.65 0.45 0.33不够纯净在用于纯净的容器内b的真空度过低(a6=0.94)准确7月31日0.21 0.40 0.18 0.870.95 0.78 0.76喷射口没有液体闭阀不运动(a2=1.00)错误8月9日0.93 0.43 0.22 0.300.87 0.32 0.27比例不稳定,量子浮动单

15、方向阀存在一个运动错误(a1=0.85)准确8月21日0.25 0.46 0.22 0.870.81 0.33 0.35喷射嘴泄露量子浮动在喷射厅内存在液体泄露 (a4=0.94)准确8月30日0.91 0.50 0.93 0.720.94 0.91 0.33不够纯净,量子减少比例不稳定,量子浮动在用于纯净的容器b中的液位过低(a5=1.00)准确9月6日0.80 0.30 0.94 0.680.88 0.45 0.36比例不稳定,不够纯净,量子浮动喷射厅有液体泄露 (a3=0.95)准确10月5日0.26 0.43 0.22 0.610.81 0.67 0.46量子浮动闭阀的压力过低(a2=

16、0.75)准确11月14日0.27 0.47 0.20 0.800.72 0.35 0.38量子浮动喷射厅内有液体泄露(a4=0.89)准确5结论这种vrsde的错误自动诊断方式是建立在模糊数学的基础之上的,通过使用建立在数学模型上的vrsde错误自动诊断系统,我们实现了对设备运行状态和错误自动诊断系统的时实监控.系统的使用效果说明此系统有效的满足了设计要求和顾客的期望.这种错误诊断数学模型涉及许多主观的参数,阀的数值都由经验所得.因此,数值必须通过无数次的实验核实和调整,从而和vrsde的实际想吻合.参考资料1.a.h.张, 运行状态监控技术与机电一体化设备的故障诊断. 西北科技大学出版社

17、1995(中国)2.z.w.何,vrsde故障诊断系统的主要研究(主体性论文). 西安理工大学 2001(中国) 3.a.p.陈 和 c.c.林 故障诊断的模糊方法. 模糊集合及其系统. vol.118,pp.139-151.2001简历何正文现任安交通大学管理学院博士,其研究领域包括工业工程和erp.徐玉现任西安交通大学管理学院教授专家,她的研究领域包括综合管理,企业优化,科技资源和科学的有效融合.吴军现任西安交通大学管理学院博士,他的研究领域涉及综合管理,企业优化,金融工程.a fuzzy mathematics based fault auto-diagnosis system for

18、vacuum resin shot dosing equipmenthe zheng-wen , xu yu , wu junschool of management, xian jiaotong univervsity, xian 710049, p.r. chinaabstract: on the basic of the analysis of faults and their causes of vacuum resin shot dosing equipment, the fuzzy model of fault diagnosis for the equipment is cons

19、tructed, and the fuzzy relationship matrix, the symptom fuzzy vector, the fuzzy compound arithmetic operator, and the diagnosis principle of the model are determined. then the fault auto-diagnosis system for the equipment is designed, and the functions for real-time monitoring its operation conditio

20、n and for fault auto-diagnosis are realized. finally, the experiments of fault auto-diagnosis are conducted in practical production and the veracity of the system is verified.key words: fuzzy model, fault auto-diagnosis system, vacuum resin shot dosing equipment1 introductionvacuum resin shot dosing

21、 equipment (vrsde) is a key special equipment used to perfuse various kinds of electrical components, such as striking windings of vehicles and motorcycles, transformers, sensors, capacitors and so on (hereafter referred to as “workpieces” ) with epoxy resin. its function is to purify and to mix epo

22、xy resin (liquid a) with solidifying catalyst (liquid b) under a vacuum condition firstly, and then to perfuse them into workpieces in accurate proportion and quantum, thus finishing epoxy resin perfusion.similar to most complicated equipment, the fault information environment of vrsde is a fuzzy on

23、e, basically. beginning with analyzing the relationship of faults and their causes, we construct the mathematical model of faults and their causes, we construct the mathematical model of fault diagnosis for the vrsde based on fuzzy theory. on the basis of this model we build a fault auto-diagnosis s

24、ystem for the equipment that fulfills the functions of real-time monitoring on the operation condition and fault auto-diagnosis of the equipment. the veracity of the system is verified in practical productions.2 the main faults and their causesaccording to the impacts on the quality and the process

25、of perfusion, the main faults of the vrsde can be classified as the following kinds: proportion fluctuation, insufficient mixture, inadequate purification, leaking from shot mouth, quantum fluctuation, quantum reduction and no liquid shooting out from shot mouth. our research on the formation mechan

26、ism of faults shows that these faults have relations on the following reasons to different extents: uni-direction valve being in malfunction state ,shutting-valve being in malfunction state, the pipe out of the perfusion chamber seeping, the pipe within the perfusion chamber seeping, liquid level in

27、 purifying canister being too low, vacuum degree of purifying canister being too low, liquid temperature fluctuation, moving down speed of the cylinder being too low, air pressure being too low, measuring pump wearing and tearing seriously, machining precision of measuring system being too low, prop

28、ortion being too big or too small and resin containing arenaceous quartz. the correlative degree of faults and their causes can be described by table 1 qualitatively.table 1 the correlative degree of main faults and their causes of vrsdeproportionfluctuationinsufficientmixtureinadequatepurificationl

29、eakagefrom shotmouthquantumfluctuationquantumreductionno liquid shooting out from the shoot mouthuni-direction valve being in malfunction state/shutting-valve being in malfunction state/the pipe out of perfusion chamber seeping/the pipe out within perfusion chamber seeping/liquid level in purifying

30、canister being too low/vacuum degree of purifying canister being too low/liquid temperature fluctuation/moving down speed of the cylinder being too low/air pressure being too low/measuring pump wearing and tearing seriously/machining precision of measuring system being too low/proportion being too b

31、ig or too small/resin containing arenaceous quartz/note: the number of “ “ indicates the correlative degree of causes and faults.3 construction of fuzzy model for fault diagnosiswe define that the operation condition set of the vrsde consists of all kinds of faults and is expressed by v: v1 proporti

32、on fluctuation v2 insufficient mixturev3 inadequate purificationv = v4 = leakage from shot mouthv5 quantum fluctuation v6 quantum reductionv7 no liquid shooting out from shot mouth the symptom set of vrsed is composed of all reasons of faults and defined as u u1 uni-direction valve being in malfunct

33、ion state u2 shutting-valve being in malfunction state u3 the pipe out of the perfusion chamber seeping u4 the pipe within the perfusion chamber seeping u5 liquid level in purifying canister being too low u6 vacuum degree of purifying canister being too low u = u7 = liquid temperature fluctuation u8

34、 moving down speed of the cylinder being too low u9 air pressure being too low u10 measuring pump wearing and tearing seriously u11 machining precision of measuring system being too low u12 proportion being too big or too small u13 resin containing arenaceous quartzthe fuzzy relationship matrix of o

35、perating condition set v and symptom set u is defined as r: r11 r12 . r17 r = r21 r22 . r27 . . . . r13.1 r13.2 r13.4where rij=uji, 0rij1, 1i13 , 1j7 , rij denotes the correlative degree of operating condition j and symptom i of the vrsde.through dealing with the data gathered by sensors , we can ob

36、tain symptom fuzzy vector a:a=a1/u1+a2/u2+.+a13/u13=(a1,a2,.,a13) 0ai1 i=1,2,.,13 vector a is the subset of symptom set u and its element ai represents the membership of ui to vector a. element ai indicates the effect degree of symptom ui on the total symptom of the equipment, or in a common term, s

37、ignifies whether cause i is serious or not to a certain extent.after symptom fuzzy vector a and fuzzy relationship matrix r are obtained, condition fuzzy vector b can be calculated using the following formula:b= a . rwhere “.” represent fuzzy compound arithmetic operation. vector b is the subset of

38、symptom set v and can be written as follows:b=b1/v1+b2/v2+.+b7/v7=(b1,b2,.b7)where 0 =bj=1, j=1,2,.,7. element bj denotes the membership of vj to vector b and indicates the serious degree of fault j.after getting condition fuzzy vector b, we can identify the operation state of the vrsde and diagnose

39、 its faults according to the value of element b, and the fault diagnosis principle.the course above can be illustrated by figure1.working out the determination rules of symptom fuzzy vector a experiential data, experiment data and statistical data gathering data by sensors determining the correlativ

40、e degree of operation condition and symptom obtaining symptom fuzzy vector athrough dealing with the data determining fuzzy relationship matrix r determining condition fuzzy vector b through formula b=a*rdetermining fuzzy compound arithmetic operator determining principle of fault diagnosisidentifyi

41、ng operation condition and diagnosing faults outputting diagnosis results and relative information figure 1 fault diagnosis flow chart3.1 determination of fuzzy relationship matrix rin our research, we adopt an integrated technique combining several methods such as expert experience method, statisti

42、cal method of experiments, twice two-element comparison method together to determine elements values of the fuzzy relationship matrix. on the basic of the experiences accumulated through innumerable experiments, the value of every element is given primarily by using the twice two-element comparison

43、method. then we verify and adjust these values continuously until they coincide with the real relationship of the cause and the faults of the vrsde. the fuzzy relationship matrix r is determined as follows:0.96 0.98 0.00 0.00 0.85 0.00 0.001.0 0.00 0.00 0.95 0.98 0.84 0.630.79 0.32 0.98 0.71 0.82 0.18 0.