外文翻譯--針對(duì)真空樹脂灌注機(jī)鏡頭設(shè)備建立在自動(dòng)診斷系統(tǒng)上的模糊數(shù)學(xué)

1、<p>　　A Fuzzy Mathematics Based Fault Auto-diagnosis System for Vacuum Resin Shot Dosing Equipment</p><p>　　HE Zheng-wen , XU yu , WU Jun</p><p>　　School of Management, Xi’an Jiaoton

2、g univervsity, Xi’an 710049, P.R. China</p><p>　　Abstract: 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

3、is constructed, 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 e

4、quipment is designed, and the functions for real-time monitoring its operation condition and for fault auto-diagnosis are </p><p>　　Key words: fuzzy model, fault auto-diagnosis system, vacuum resin shot dosi

5、ng equipment</p><p>　　1 Introduction</p><p>　　Vacuum Resin Shot Dosing Equipment (VRSDE) is a key special equipment used to perfuse various kinds of electrical components, such as striking windi

6、ngs 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 epoxy resin (liquid A) with solidifying catalys

7、t (liquid B) under a vacuum condition firstly, and then to perfuse them into workpieces in accurate proportion and quantum, thus fin</p><p>　　Similar to most complicated equipment, the fault information envi

8、ronment of VRSDE is a fuzzy one, 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 fau

9、lt diagnosis for the VRSDE based on fuzzy theory. On the basis of this model we build a fault auto-diagnosis system for the equipment that fulfills the functions of real-time monitoring on the operation condition an</

10、p><p>　　2 The main faults and their causes</p><p>　　According to the impacts on the quality and the process of perfusion, the main faults of the VRSDE can be classified as the following kinds: prop

11、ortion 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 mechanism of fa

12、ults shows that these faults have relations on the following reasons to different extents: uni-direction valve being in malfunction sta</p><p>　　Table 1 The correlative degree of main faults and their causes

13、 of VRSDE</p><p>　　Note: The number of “○ “ indicates the correlative degree of causes and faults.</p><p>　　3 construction of fuzzy model for fault diagnosis</p><p>　　We define tha

14、t the operation condition set of the VRSDE consists of all kinds of faults and is expressed by V: </p><p>　　V1 Proportion fluctuation </p><p>　　V2 Insufficient mixture</p>

15、<p>　　V3 Inadequate purification</p><p>　　V = V4 = Leakage from shot mouth</p><p>　　V5 Quantum fluctuation </p><p>　　V6 Quantum reduction<

16、/p><p>　　V7 No liquid shooting out from shot mouth </p><p>　　The symptom set of VRSED is composed of all reasons of faults and defined as U</p><p>　　U1 Uni-direction val

17、ve being in malfunction state</p><p>　　U2 Shutting-valve being in malfunction state</p><p>　　U3 The pipe out of the perfusion chamber seeping </p><p>　　U4 T

18、he pipe within the perfusion chamber seeping </p><p>　　U5 Liquid level in purifying canister being too low</p><p>　　U6 Vacuum degree of purifying canister being too low</p>

19、<p>　　U = u7 = Liquid temperature fluctuation</p><p>　　U8 Moving down speed of the cylinder being too low</p><p>　　U9 Air pressure being too low</p><p>　

20、　U10 Measuring pump wearing and tearing seriously</p><p>　　U11 Machining precision of measuring system being too low</p><p>　　U12 proportion being too big or too small</p>

21、;<p>　　U13 Resin containing arenaceous quartz</p><p>　　The fuzzy relationship matrix of operating condition set V and symptom set U is defined as R:</p><p>　　r11 r12 ......

22、 r17</p><p>　　R = r21 r22 ...... r27</p><p>　　.... ... ... ...</p><p>　　r13.1 r13.2 … r13.4</p><p>　　where rij=uji, 0≦rij≦1, 1≦i≦13 , 1

23、≦j≦7 , rij denotes the correlative degree of operating condition j and symptom i of the VRSDE.</p><p>　　Through dealing with the data gathered by sensors , we can obtain symptom fuzzy vector A:A=a1/u1+a2/u2+

24、...+a13/u13=(a1,a2,...,a13) 0≦ai≦1 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

25、symptom of the equipment, or in a common term, signifies whether cause i is serious or not to a certain extent.</p><p>　　After symptom fuzzy vector A and fuzzy relationship matrix r are obtained, condition f

26、uzzy vector B can be calculated using the following formula:</p><p><b>　　B= A . R</b></p><p>　　Where “.” Represent fuzzy compound arithmetic operation. Vector B is the subset of symp

27、tom set V and can be written as follows:</p><p>　　B=b1/v1+b2/v2+...+b7/v7=(b1,b2,....b7)</p><p>　　Where 0 <=bj<=1, j=1,2,....,7. Element bj denotes the membership of vj to vector B and ind

28、icates the serious degree of fault j.</p><p>　　After getting condition fuzzy vector B, we can identify the operation state of the VRSDE and diagnose its faults according to the value of element b, and the fa

29、ult diagnosis principle.</p><p>　　The course above can be illustrated by Figure1.</p><p>　　Figure 1 Fault diagnosis flow chart</p><p>　　3.1 Determination of fuzzy relationship matri

30、x R</p><p>　　In our research, we adopt an integrated technique combining several methods such as expert experience method, statistical method of experiments, twice two-element comparison method together to d

31、etermine 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 compar

32、ison method. Then we verify and adjust these values continuously until they coincide w</p><p>　　0.98 0.00 0.00 0.85 0.00 0.00</p><p>　　0.00 0.00 0.95 0.98 0.84 0.63</p><p>

33、;　　0.32 0.98 0.71 0.82 0.18 0.00</p><p>　　0.00 0.00 0.89 0.80 0.37 0.00</p><p>　　0.42 0.96 0.68 0.92 0.95 0.00 </p><p>　　0.45 0.35 0.97 0.67 0.68 0.20 0.00<

34、/p><p>　　R= 0.34 0.47 0.31 0.32 0.55 0.00 0.00 </p><p>　　0.92 0.00 0.00 0.00 0.00 0.00</p><p>　　0.91 0.00 0.00 0.00 0.00 0.82 </p><p>　　0.30 0.00

35、 0.00 0.90 0.15 0.00</p><p>　　0.00 0.00 0.00 0.90 0.00 0.00</p><p>　　0.71 0.00 0.00 0.00 0.00 0.00</p><p>　　0.52 0.23 0.00 0.21 0.31 0.45</p><p>　　3.2

36、 Determination of symptom fuzzy vector A</p><p>　　Element a (i =1, 2 ...., 13 ) of symptom fuzzy vector A can be determined by dealing with the data gathered by sensors. For the elements whose definition fie

37、lds are a real number field, we may select their membership functions according to their characteristics and the traits of the fuzzy set. The membership functions of these elements are shown in Table 2. For other element

38、s, we can determine their membership values according to the rules listed in Table 3. </p><p>　　Determination of fuzzy compound arithmetic operator</p><p>　　The results of the causes on the faul

39、ts are very complex. For a certain kind of fault, its causes not only can be divided into primary ones and secondary ones but also can exert an integrated influence on the fault as well. Furthermore, there still exists a

40、 strong correlation among various causes. For the facts above, we adopt a fuzzy compound arithmetic operator of “giving prominence to the main symptom” as the fuzzy compound calculation, thus not only attaching an import

41、ance to the decisive ef</p><p>　　bj=V( ai*rij) j=1,2,.....,7</p><p>　　Table 2 The membership functions of symptom fuzzy vector A’s elements whose definition fields are real number field

42、</p><p>　　Table3 Membership determination rules of symptom fuzzy vector A’s elements whose definition fields are not real number field</p><p>　　Determination of fault diagnosis principle </p

43、><p>　　In order to identify the operation conditions of the VRSDE accurately and to offer sufficient information related to the faults, we define two threshold valves, λ1 andλ2 (λ1<λ2), and classify its oper

44、ation states as the following three kinds: Normal state , pre-warning state and malfunction state. The principle of fault fuzzy diagnosis can be described as follows:</p><p>　　When max (bj) ≦λ1 ,VRSDE is in

45、pre-warning state;</p><p>　　Whenλ1<max(bj) ≦λ2 ,VRSDE is in pre-warning state;</p><p>　　When max (bj)> λ2,VRSDE is in malfunction state.</p><p>　　When the equipment is in a pr

46、e-warning or malfunction state ,the signals of pre-warning or warning are sent out to warn the operators and the fault kinds and their relative information are provided by the system at the same time. The valve ofλ1 and

47、λ2 can determination by iterative experiments and are given ultimately as follows:</p><p>　　λ1=0.50, λ2=0.80</p><p>　　Fault auto-diagnosis experiments</p><p>　　After

48、constructing the mathematical model of fault diagnosis, we establish the fault auto-diagnosis system for the VRSDE by utilizing hardware such as sensors, data gathering circuits, computer, warning circuits and correspond

49、ing software, thus realizing the function of monitoring on the equipment’s operation condition, fault diagnosis, giving alarms, showing the relative information, etc.</p><p>　　The mathematical model of the f

50、ault auto-diagnosis system for the VRSDE is constructed on the basis of experiences accumulates by a great number of experiments, so we have to carry out experiments to verify its correctness. The method of the experimen

51、ts can be described as follows: during the practical operation of the equipment, if the system gives an alarm and indicates that there is a certain fault, we may carry out a special test to check whether the fault exists

52、 or not, thus the veracity of</p><p>　　During half a year from May 2000 to November 2000, we carried out a series of experiments of fault diagnosis with a VCD-M3 VRSDE in Northwestern Forest Machine Limited

53、Company and obtained the data shown in Table 4.From these data, we can see that the correct percentage of that diagnosis has reached 93.3% and the design requirements of the system have been met, basically. Then the corr

54、ectness of the mathematical model of fault diagnosis can be validated indirectly by this result too. Within the </p><p>　　Table 4 The data of fault auto-diagnosis experiments</p><p>　　5 Conclu

55、sion </p><p>　　The fault auto-diagnosis model for the VRSDE is constructed based on fuzzy mathematics, and the function of real-time monitoring on operation condition and auto-diagnosis faults of the equipme

56、nt are realized by using the fault auto-diagnosis system for the VRSDE which is formed on the basis of the mathematical model. The application effects of the system show that the system attains the design purposed and me

57、ets customers’ expectations satisfactorily. The mathematical model of fault diagnosis is</p><p>　　References</p><p>　　1．A.H. Zhang, Technologies of monitoring on operation condition and fault di

58、agnosis for mechatronic equipment. Northwestern Poly technical University Press, 1995 (In Chinese)</p><p>　　2. Z.W. He, Primary research on the fault auto-diagnosis system for VRSDE: (Mastership Dissertation

59、). Xi’an University of Technology, 2001(In Chinese)</p><p>　　3. A.P. Chen and C.C. Lin, Fuzzy approaches for fault diagnosis of trans formers. Fuzzy Sets and Systems, Vol.118, pp.139-151, 2001</p><

60、;p>　　Brief Biographies </p><p>　　HE Zheng-wen is now a Ph. D candidate in the school of Management of Xi’an Jiaotong University, his research interests include industry engineering and ERP.</p><

61、;p>　　XU Yu is now a professor in the School of Management of Xi’an Jiaotong University, her research interests include integrated management and optimization of enterprises, and optimal collocation of science and tech

62、nology resources.</p><p>　　WU Jun is now a Ph. D candidate in the School of Management of Xi’an Jiantong University, his research interests include integrated management optimization of enterprises, and fina

63、ncial engineering.</p><p>　　針對(duì)真空樹脂灌注機(jī)鏡頭設(shè)備建立在自動(dòng)診斷系統(tǒng)上的模糊數(shù)學(xué)</p><p>　　何政文 許鈺 吳俊</p><p>　　西安交通大學(xué)管理學(xué)院，中國(guó)西安710049</p><p>　　摘要：在分析真空樹脂鏡頭藥設(shè)備的基礎(chǔ)之上，模糊數(shù)學(xué)模型已經(jīng)建立起來，并且模糊關(guān)系矩陣，癥狀

64、模糊向量，模糊復(fù)合運(yùn)算操作，模型的診斷原則已經(jīng)確定。接著，設(shè)備的錯(cuò)誤自動(dòng)診斷系統(tǒng)也被設(shè)計(jì)完成，實(shí)時(shí)監(jiān)控狀態(tài)的功能和故障自動(dòng)診斷可以得以實(shí)現(xiàn)。最后，故障自動(dòng)診斷在實(shí)際生產(chǎn)下完成實(shí)驗(yàn)并且系統(tǒng)的真實(shí)性得到驗(yàn)證。</p><p>　　關(guān)鍵詞：模糊模型，故障自動(dòng)檢測(cè)系統(tǒng)，真空樹脂灌注機(jī)設(shè)備</p><p><b>　　1引言</b></p><p>　　

65、真空樹脂灌注機(jī)設(shè)備（VRSDE）特別主要是用于各種電器元件的灌注，比如汽車、摩托車的打繞組，變壓器，傳感器，電容器等配有環(huán)氧樹脂的器件（一下簡(jiǎn)稱“工件”）。它的作用首先是凈化和把環(huán)氧樹脂（液態(tài)甲）和固態(tài)催化劑（液態(tài)乙）在真空下合成，接著把他們按照精確的比例和量注射到工件中，這樣就完成了環(huán)形樹脂的注射。</p><p>　　基本上，和大多數(shù)復(fù)雜的設(shè)備一樣，VRSDE的錯(cuò)誤信息環(huán)境是模糊的。從分析錯(cuò)誤和他們起因的關(guān)系

66、開始，我們可以建立錯(cuò)誤和起因之間的數(shù)學(xué)模型，同樣我們可以在模糊原理的基礎(chǔ)上對(duì)VRSDE建立數(shù)學(xué)模型 。在這個(gè)模型基礎(chǔ)上我們建立了設(shè)備的故障自動(dòng)檢測(cè)系統(tǒng)已達(dá)到實(shí)時(shí)監(jiān)控運(yùn)行狀態(tài)和設(shè)備故障診斷的功能.系統(tǒng)的真實(shí)性在實(shí)際操作中得到驗(yàn)證.</p><p>　　2主要的錯(cuò)誤和他們的起因</p><p>　　根據(jù)注射質(zhì)量和過程的影響,VRSDE的主要錯(cuò)誤可以歸結(jié)成以下幾種:比列的浮動(dòng),不充分混合,不夠凈

67、化,注射口的泄露,量子的不穩(wěn)定,量子的減少和沒有液體從射擊口出來.我們?cè)趯?duì)錯(cuò)誤形成機(jī)理的研究表明在不同程度上錯(cuò)誤的發(fā)生和以下的原因有關(guān): 單方向閥處于故障狀態(tài)中，關(guān)閥處于故障狀態(tài)中，注射器噴灑時(shí)液體的泄露，注射器內(nèi)的液體滲漏，用于凈化的容器內(nèi)的液位或真空過低，液體溫度的不穩(wěn)定，缸向下移動(dòng)的速度過低，氣壓過低，計(jì)量泵磨損嚴(yán)重，測(cè)量系統(tǒng)的機(jī)器精度過低，比例過大或過小并且樹脂含有石英。錯(cuò)誤及其原因的相關(guān)程度可以定性的描述成表1。</p&

68、gt;<p>　　表1 VRSDE的主要錯(cuò)誤及其原因的相對(duì)程度</p><p>　　說明：“○”的數(shù)量表示錯(cuò)誤及其原因的相對(duì)度</p><p>　　3錯(cuò)誤診斷的模糊模型的結(jié)構(gòu)</p><p>　　我們確定了VRSDE運(yùn)行狀態(tài)中的各種故障，用V來發(fā)表：</p><p>　　V1 比例浮動(dòng) </p>

69、<p>　　V2 不充分混合</p><p>　　V3 不夠凈化</p><p>　　V = V4 = 噴射嘴泄露</p><p>　　V5 量子浮動(dòng) </p><p>　　V6 量子減少</p><p>　　V7

70、 沒有液體從噴射嘴噴出 </p><p>　　而引起各種錯(cuò)誤癥狀的原因，我們用U表示：</p><p>　　U1 單方向閥故障狀態(tài)</p><p>　　U2 閉閥故障狀態(tài)</p><p>　　U3 噴射廳的液體泄露 </p><p>　　U4 注射

71、器內(nèi)的液體泄露 </p><p>　　U5 用于凈化的容器內(nèi)的液位過低</p><p>　　U6 用于凈化的容器內(nèi)的真空過低</p><p>　　U = u7 = 液體溫度不穩(wěn)定</p><p>　　U8 缸向下運(yùn)動(dòng)的速度太低</p><p>　　U9

72、 氣體壓力太低</p><p>　　U10 計(jì)量泵磨損嚴(yán)重</p><p>　　U11 測(cè)量系統(tǒng)的機(jī)器精度過低</p><p>　　U12 比例過大或過小</p><p>　　U13 樹脂含有石英</p><p>　　運(yùn)行狀態(tài)U和癥狀V之間的模糊關(guān)系可以用矩陣R來表

73、示:</p><p>　　r11 r12 ...... r17</p><p>　　R = r21 r22 ...... r27</p><p>　　.... ... ... ...</p><p>　　r13.1 r13.2 … r13.4</p>

74、;<p>　　當(dāng)rij=uji, 0≦rij≦1, 1≦i≦13 , 1≦j≦7 時(shí), rij 定義為VRSDE的運(yùn)行狀態(tài)j和癥狀i的相對(duì)度.</p><p>　　通過對(duì)傳感器數(shù)據(jù)的處理,我們可以得到癥狀模糊矩陣:</p><p>　　A:A=a1/u1+a2/u2+...+a13/u13=(a1,a2,...,a13) 0≦ai≦1 i=1,2,...,13</p

75、><p>　　A是癥狀U的一個(gè)子集,并且它的元素ai代表u到集合a的映射.元素ai表示癥狀ui在總的癥狀中的有效程度,或者從某種意義上講代表原因i嚴(yán)重與否.</p><p>　　在模糊矩陣A和模糊關(guān)系矩陣r得到之后,狀態(tài)模糊矩陣B可以從下面的公式算得.</p><p><b>　　B=A*R</b></p><p>　　“

76、*”代表模糊復(fù)合運(yùn)算.矩陣B是癥狀V的一個(gè)子集,可由下列公式運(yùn)算得到:</p><p>　　B=b1/v1+b2/v2+...+b7/v7=(b1,b2,....b7)</p><p>　　公式中0 <=bj<=1, j=1,2,....,7.元素bj代表vj 到矩陣B的映射,并且表示錯(cuò)誤j的嚴(yán)重程度.在我們得到狀態(tài)模糊矩陣之后,我們可以鑒定VRSDE的操作狀態(tài)并且根據(jù)元素b和

77、錯(cuò)誤診斷原則來診斷他的錯(cuò)誤.</p><p>　　上述過程可以用表1來說明.</p><p><b>　　圖1錯(cuò)誤診斷流程圖</b></p><p>　　3．1模糊關(guān)系矩陣R的研究</p><p>　　在我們的研究中，我們采用了由多種方法如：專家經(jīng)驗(yàn)，實(shí)驗(yàn)數(shù)據(jù)，兩次兩元素比較等多種方法相結(jié)合的綜合技術(shù)，然后確定在矩陣中元

78、素的值。以無數(shù)次實(shí)驗(yàn)中得到的經(jīng)驗(yàn)為基礎(chǔ)，每個(gè)元素的值主要是靠?jī)纱蝺稍乇容^法來得到。接著我們不斷調(diào)整和核實(shí)這些數(shù)值直到他們和VRSDE中錯(cuò)誤和原因之間的關(guān)系矩陣相吻合。模糊關(guān)系矩陣R被定義為如下：</p><p>　　0.98 0.00 0.00 0.85 0.00 0.00</p><p>　　0.00 0.00 0.95 0.98 0.84 0.63</p&g

79、t;<p>　　0.32 0.98 0.71 0.82 0.18 0.00</p><p>　　0.00 0.00 0.89 0.80 0.37 0.00</p><p>　　0.42 0.96 0.68 0.92 0.95 0.00 </p><p>　　0.45 0.35 0.97 0.67 0.68 0.

80、20 0.00</p><p>　　R= 0.34 0.47 0.31 0.32 0.55 0.00 0.00 </p><p>　　0.92 0.00 0.00 0.00 0.00 0.00</p><p>　　0.91 0.00 0.00 0.00 0.00 0.82 </p><p>　

81、　0.30 0.00 0.00 0.90 0.15 0.00</p><p>　　0.00 0.00 0.00 0.90 0.00 0.00</p><p>　　0.71 0.00 0.00 0.00 0.00 0.00</p><p>　　0.28 0.52 0.23 0.00 0.21 0.31 0.45</p&g

82、t;<p>　　3.2癥狀模糊矩陣A的確定</p><p>　　可以對(duì)傳感器收集到的數(shù)據(jù)的處理得到癥狀模糊矩陣A中的元素a(i =1, 2 ...., 13 ).由于這些元素的定義域?yàn)閷?shí)數(shù)域，我們可以根據(jù)模糊集合的特點(diǎn)和性狀選擇元素。這些元素的值可查詢表2。對(duì)于其他一些元素我們可以根據(jù)表3確定他們的數(shù)值。</p><p>　　3．3模糊復(fù)合運(yùn)算的確定</p>&

83、lt;p>　　引起錯(cuò)誤的原因十分復(fù)雜。一些典型錯(cuò)誤的原因不僅可以分為主要的和次要的，并且也可以對(duì)錯(cuò)誤產(chǎn)生綜合的影響。此外，在多種原因中還存在復(fù)雜的聯(lián)系。基于上述事實(shí)，我們采用了模糊復(fù)合運(yùn)算操作“優(yōu)先考慮主要癥狀”稱為模糊復(fù)合運(yùn)算。因此不僅高度重視主要錯(cuò)誤的決定性因素，也考慮到很多次要的方面。根據(jù)這種模糊復(fù)合運(yùn)算操作，我們可以由下面的公式得出矩陣B中的元素b:</p><p>　　bj=V( ai*rij)

84、 j=1,2,.....,7</p><p>　　表2定義在實(shí)數(shù)域上癥狀模糊矩陣A中的元素的值 </p><p>　　表3 癥狀模糊矩陣A中定義域不是在實(shí)數(shù)范圍的元素的確定</p><p>　　3．4錯(cuò)誤診斷原則的確定</p><p>　　為了準(zhǔn)確的鑒定的VRSDE操作狀態(tài)并且提供和錯(cuò)誤有關(guān)的足夠多的信息,我們定義了兩個(gè)值λ1

85、和λ2 (λ1<λ2),并把它的工作狀態(tài)歸結(jié)為以下三類:正常狀態(tài),提前警告狀態(tài)和故障狀態(tài).錯(cuò)誤模糊診斷的原則可以描述成: </p><p>　　當(dāng)max (bj) ≦λ1時(shí), VRSDE處于正常狀態(tài)</p><p>　　當(dāng)λ1<max(bj) ≦λ2 VRSDE時(shí),處于提前警告狀態(tài)</p>

86、;<p>　　當(dāng)max (bj)> λ2時(shí).VRSDE處于故障狀態(tài)</p><p>　　當(dāng)設(shè)備處于提前警告狀態(tài)或故障狀態(tài)時(shí),會(huì)發(fā)出預(yù)警信號(hào)或警告信號(hào)來警告操作同時(shí)系統(tǒng)會(huì)提供錯(cuò)誤及其相關(guān)信息.實(shí)驗(yàn)表明λ1 和λ2的最大值如下:</p><p>　　λ1 =0.50 λ2 =0.80</p><p><b>　　4錯(cuò)誤自動(dòng)

87、診斷實(shí)驗(yàn)</b></p><p>　　在建立錯(cuò)誤診斷的數(shù)學(xué)模型之后,我們利用諸如像傳感器這樣的硬件,數(shù)據(jù)采集器,電腦,報(bào)警線路和相關(guān)的軟件建立了VRSDE的錯(cuò)誤自動(dòng)診斷系統(tǒng),這樣就實(shí)現(xiàn)了對(duì)設(shè)備操作條件,錯(cuò)誤診斷,相關(guān)錯(cuò)誤的監(jiān)控并且顯示出相關(guān)的信息等.</p><p>　　錯(cuò)誤自動(dòng)診斷系統(tǒng)的數(shù)學(xué)模型建立在無數(shù)次實(shí)驗(yàn)得來的經(jīng)驗(yàn)的基礎(chǔ)上,因此我們必須進(jìn)行實(shí)驗(yàn)來證實(shí)其準(zhǔn)確性.實(shí)驗(yàn)的方法

88、可以描述成如下:通過對(duì)設(shè)備實(shí)際操作,如果系統(tǒng)給出一個(gè)警告并且暗示設(shè)備存在錯(cuò)誤,我們可以進(jìn)行特殊的試驗(yàn)來檢測(cè)錯(cuò)誤存在與否,這樣可以保證系統(tǒng)準(zhǔn)確無誤.此外,我們可以有周期的檢查VRSDE的工作參數(shù)來確定設(shè)備是否存在自身不能識(shí)別出來的錯(cuò)誤.</p><p>　　在2000年5月到2000年11月的半年時(shí)間里，我們?cè)谖鞅绷謽I(yè)機(jī)械股份有限公司針對(duì)VCD-M3 VRSDE進(jìn)行了一系列實(shí)驗(yàn)，從中得到了表4的數(shù)據(jù)。從這些數(shù)據(jù)中我

89、們可以看出診斷的正確率達(dá)到了93.3%，系統(tǒng)的設(shè)計(jì)也基本達(dá)到要求。那么錯(cuò)誤診斷的數(shù)學(xué)模型同樣可以間接從這結(jié)果中得到證實(shí)。在表格中可以看出在2000年月31日有一個(gè)錯(cuò)誤診斷。這一錯(cuò)誤的原因是控制閉閥位置的活塞震蕩過，從而活塞被固定住，不能動(dòng)彈。這種現(xiàn)象發(fā)生的可能性極小，甚至可以稱為偶然。除了上述事實(shí)，我們?nèi)匀幻恐軠y(cè)試一次VRSDE的工作參數(shù)</p><p>　　并且在這段時(shí)間內(nèi)沒有發(fā)現(xiàn)系統(tǒng)自身檢測(cè)不了的問題.<

90、;/p><p>　　表4 錯(cuò)誤自動(dòng)診斷實(shí)驗(yàn)的數(shù)據(jù)</p><p><b>　　5結(jié)論</b></p><p>　　這種VRSDE的錯(cuò)誤自動(dòng)診斷方式是建立在模糊數(shù)學(xué)的基礎(chǔ)之上的,通過使用建立在數(shù)學(xué)模型上的VRSDE錯(cuò)誤自動(dòng)診斷系統(tǒng),我們實(shí)現(xiàn)了對(duì)設(shè)備運(yùn)行狀態(tài)和錯(cuò)誤自動(dòng)診斷系統(tǒng)的時(shí)實(shí)監(jiān)控.系統(tǒng)的使用效果說明此系統(tǒng)有效的滿足了設(shè)計(jì)要求和顧客的期望.這種

91、錯(cuò)誤診斷數(shù)學(xué)模型涉及許多主觀的參數(shù),閥的數(shù)值都由經(jīng)驗(yàn)所得.因此,數(shù)值必須通過無數(shù)次的實(shí)驗(yàn)核實(shí)和調(diào)整,從而和VRSDE的實(shí)際想吻合.</p><p><b>　　參考資料</b></p><p>　　1.A.H.張, 運(yùn)行狀態(tài)監(jiān)控技術(shù)與機(jī)電一體化設(shè)備的故障診斷. 西北科技大學(xué)出版社 1995(中國(guó))</p><p>　　2.Z.W.何,VRSDE

92、故障診斷系統(tǒng)的主要研究(主體性論文). 西安理工大學(xué) 2001(中國(guó)) </p><p>　　3.A.P.陳 和 C.C.林 故障診斷的模糊方法. 模糊集合及其系統(tǒng). Vol.118,pp.139-151.2001</p><p><b>　　簡(jiǎn)歷</b></p><p>　　何正文現(xiàn)任安交通大學(xué)管理學(xué)院博士,其研究領(lǐng)域包括工業(yè)工程和ERP.&