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 A Fuzzy Mathematics Based Fault Auto-diagnosis System for Vacuum Resin Shot Dosing Equipment HE Zheng-wen , XU yu , WU Jun School of Management, Xian Jiaotong univervsity, Xian 710049, P.R. China 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 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 equipment is designed, and the functions for real-time monitoring its operation condition 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 equipment 1 Introduction Vacuum Resin Shot Dosing 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 epoxy 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 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 fault diagnosis for the VRSDE based on fuzzy theory. On 2 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 and fault auto-diagnosis of the equipment. The veracity of the system is verified in practical productions. 2 The main faults and their causes 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: 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 mechanism 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 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, proportion 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 VRSDE Proportion fluctuation Insufficient mixture Inadequate purification Leakage From shot mouth Quantum fluctuation Quantum reduction No liquid shooting out from the shoot mouth Uni-direction valve being in malfunction state / / / / Shutting-valve being in malfunction state / / / The pipe out of perfusion chamber / 3 seeping The pipe out within perfusion chamber seeping / / / / Liquid level in 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 / / / / / Proportion being too big 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 diagnosis We define that the operation condition set of the VRSDE consists of all kinds of faults 4 and is expressed by V: V1 Proportion fluctuation V2 Insufficient mixture V3 Inadequate purification V = V4 = Leakage from shot mouth V5 Quantum fluctuation V6 Quantum reduction V7 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 malfunction 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 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 quartz The fuzzy relationship matrix of operating condition set V and symptom set U is defined as R: r11 r12 . r17 R = r21 r22 . r27 . . . . r13.1 r13.2 r13.4 where rij=uji, 0 rij 1, 1 i 13 , 1 j 7 , 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 obtain symptom fuzzy vector A:A=a1/u1+a2/u2+.+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 symptom of the equipment, or in a common term, signifies whether cause i is serious or not to a certain extent. After symptom fuzzy vector A and fuzzy relationship matrix r are obtained, condition 5 fuzzy vector B can be calculated using the following formula: B= A . R Where “.” Represent fuzzy compound arithmetic operation. Vector B is the subset of symptom set V and can be written as follows: B=b1/v1+b2/v2+.+b7/v7=(b1,b2,.b7) Where 0 =bj a4 Membership of the Pipe within the Perfusion chamber Seeping to the Vector A Absolute difference Between the liquid pressure At the pipe joints and the Vacuum degree within The perfusion chamber(p) Down-Half-T membership function: 1 P a4= 1-e-k(P-) Pi a6 Membership of Vacuum degree of Purifying canisters Being too low to the Vector A Vacuum degree of purifying canisters(Pi) Up-Half-Gauss membership function a6=max(a6i) 0 Pi a6i= 1-e-k(Pi-100)2 Pi a8 Membership of moving down speed of the cylinder being too low to the vector A Moving down speed of the cylinder(x) Down-Half-Trapezia membership function: 1 0 x a8= (r2-x)/(r2-r1) 12 a9 Membership of air Pressure being too Low to the vector A Air pressure (P) Down-Half-Gauss membership function: 1 P a9= e-k(P-5)2 P Table3 Membership determination rules of symptom fuzzy vector As elements whose definition fields are not real number field Element Explanation Determination rules of membership a1 Membership of uni-direction valve being in malfunction state to the vector A Motion precision of uni-direction Valve &0.05mm & 0.05mm a1=0.98 a1=0.00 Motion Synchronization of two uni-direction valves t0.5s 0.1s0.05mm a2=0.96 & 0.05mm a2=0.00 Shutting pressure of shutting-valve P4.8c a7=0.95 2.6c3.6c a7=0.93 1.9cTb4.6c a7=0.98 2.1cTmix 4.6c a7=0.65 Tmix 2.1c a7=0.00 a10 Membership of measuring Pump wearing and tearing Seriously to the vector A Resin containing arenaceous quartz a10=0.15 Resin not containing arenaceous quartz a10=0.00 a11 Membership of machining Precision of measuring system Being too low to the vector A Operation duration of VRSDE being within 2 years a11=0.08 Operation duration of VRSDE being within 2-4 years a11=0.13 Operation duration of VRSDE exceeding 4 years a11=0.25 a12 Membership of the proportion Being too big or too small to the A:B=100:100 a12=0.08 A:B=100:80 a12=0.09 9 Vector A A:B=100:34 a12=0.34 a13 Membership of resin containing Arenaceous quartz to the vector Resin containing arenaceous quartz a13=0.80 Resin not containing arenaceous quartz a13=0.00 3.4 Determination of fault diagnosis principle 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, 1 and2 (12), and classify its operation 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: When max (bj) 1 ,VRSDE is in pre-warning state； When1 2,VRSDE is in malfunction state. When the equipment is in a pre-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 of1 and2 can determination by iterative experiments and are given ultimately as follows: 1=0.50, 2=0.80 4 Fault auto-diagnosis experiments After 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 corresponding software, thus realizing the function of monitoring on the equipments operation condition, fault diagnosis, giving alarms, showing the relative information, etc. The mathematical model of the fault 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 experiments 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 or not, thus the veracity of the system is proved. In addition , we may examine the working parameters of the VRSDE periodically so as to ascertain whether there exists problems of failing to report faults or not. 10 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 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 correctness of the mathematical model of fault diagnosis can be validated indirectly by this result too. Within the table, there is a wrong diagnosis happening on July 31st, 2000. The reason for this mistake is that the lock nut of the shutting-valves piston was vibrated off and it choked up the piston and made it unable to move. The occurrence probability of this phenomenon was very little and it could be regard as a contingency. Besides the facts above, we still tested all working parameters of the VRSDE once a week and did not find any problems of failing to report faults during this period. Table 4 The data of fault auto-diagnosis experiments Warning date Information of operation condition Information of causes(ai) Correctness of diagnosis Condition fuzzy vector B Faults information May 11th (0.90 0.46 0.18 0.31 0.85 0.25 0.34) Proportion fluctuation, Quantum fluctuation The steel ball within uni-direction valve A being underlain up by the deposits of liquid A(a1 =0.98) Correct May 26th 0.93 0.42 0.96 0.68 0.92 0.95 0.36 Inadequate purification, Quantum reduction, Proportion fluctuation, Quantum fluctuation The liquid level in purifying canister A being too low (a5=1.00) Correct June 10th 0.81 0.41 0.18 0.33 0.72 0.29 0.36 Proportion fluctuation There being a time difference of the motion between two uni-direction valves(a1=0.85) Correct June 15th 0.23 0.42 0.20 0.81 0.73 0.34 0.36 Leakage from shot mouth The pipe within the perfusion chamber seeping (a4=0.91) Correct June 27th 0.32 0.46 0.90 0.65 0.66 0.44 0.37 Inadequate purification The vacuum degree of purifying canister A being too low (a6=0,94) Correct July 6th 0.23 0.42 0.20 0.91 0.91 0.94 0.78 0.56 Leakage from shot mouth Quantum fluctuation There existing a motion error of shutting-valve (a2=0.96) Correct July 13th 0.24 0.86 0.18 0.32 0.36 0.28 0.37 Insufficient mixture The pressing down speed of the cylinder being too slow (a8=0.93) Correct July 28th 0.42 0.41 0.91 0.63 0.65 0.45 0.33 Inadequate purification The vacuum degree of purifying canister B being too low (a6=0.94) Correct 11 July 31st 0.21 0.40 0.18 0.87 0.95 0.78 0.76 No liquid shooting out from shot mouth Shutting-valve not moving (a2=1.00) Wrong August 9th 0.93 0.43 0.22 0.30 0.87 0.32 0.27 Proportion fluctuation, Quantum fluctuation There existing a motion error of uni-direction valves A(a1=0.85) Correct August 21st 0.25 0.46 0.22 0.87 0.81 0.33 0.35 Leakage from shot mouth Quantum fluctuation The pipe within the perfusion chamber seeping (a4=0.94) Correct August 30th 0.91 0.50 0.93 0.72 0.94 0.91 0.33 Inadequate purification, Quantum reduction, Proportion fluctuation, Quantum fluctuation The liquid level in purifying canister B being too low (a5=1.00) Correct September 6th 0.80 0.30 0.94 0.68 0.88 0.45 0.36 Proportion fluctuation, Inadequate purification, Quantum fluctuation The pipe out of perfusion chamber seeping (a3=0.95) Correct October 5th 0.26 0.43 0.22 0.61 0.81 0.67 0.46 Quantum fluctuation The pressure of shutting-valve being too low (a2=0.75) Correct November 14th 0.27 0.47 0.20 0.80 0.72 0.35 0.38 Quantum fluctuation The pipe within the perfusion chamber seeping (a4=0.89) Correct 5 Conclusion 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 equipment 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 meets customers expectations satisfactorily. The mathematical model of fault diagnosis is involved with a great deal of subjectivity for its many parameters； threshold valves and membership functions are all determined by experiences. As a result, it has to be verified and adjusted through repetitious experiments so as to coincide with the realities of the VRSDE. References 1 A.H. Zhang, Technologies of monitoring on operation condition and fault diagnosis for mechatronic equipment. Northwestern Poly technical University Press, 1995 (In Chinese) 2. Z.W. He, Primary research on the fault auto-diagnosis system for VRSDE: (Mastership Dissertation). Xian University of Technology, 2001(In Chinese) 3. A.P. Chen and C.C. Lin, Fuzzy approaches for fault diagnosis of trans formers. 12 Fuzzy Sets and Systems, Vol.118, pp.139-151, 2001 Brief Biographies HE Zheng-wen is now a Ph. D candidate in the school of Management of Xian Jiaotong University, his research interests include industry engineering and ERP. XU Yu is now a professor in the School of Management of Xian Jiaotong University, her research interests include integrated management and optimization of enterprises, and optimal collocation of science and technology resources. WU Jun is now a Ph. D candidate in the School of Management of Xian Jiantong University, his research interests include integrated management optimization of enterprises, and financial engineering. 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) 何政文 許鈺 吳俊 西安交通大學(xué)管理學(xué)院，中國(guó)西安 710049 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(lái)，并且 模糊關(guān)系矩陣，癥狀模糊向量，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。 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 1 引言 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。 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 . 2 主要的錯(cuò)誤和他們的起因 13 根據(jù)注射質(zhì)量和過(guò)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 ,不夠凈化 ,注射口的泄露 ,量子的不穩(wěn)定 ,量子的減少和沒(méi)有液體從射擊口出來(lái) .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(guò)低，液體溫度的不穩(wěn)定，缸向下移動(dòng)的速度過(guò)低，氣壓過(guò)低，計(jì)量 泵 磨損嚴(yán)重，測(cè)量系統(tǒng)的機(jī)器精度過(guò)低，比例過(guò)大或過(guò)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。 表 1 VRSDE 的主要錯(cuò)誤及其原因的相對(duì)程度 比例 浮動(dòng) 不充分混 合 不精確凈 化 噴射嘴的 泄露 量子的波 動(dòng) 量子減少 噴射嘴中沒(méi)有液體噴出 單方向閥處于故障狀態(tài)中 / / / / 關(guān)閥處于故障狀態(tài)中 / / / 噴射廳中的液體泄露 / 注射器內(nèi)的液體滲漏 / / / / 用于凈化的容器內(nèi)的液位過(guò)低 / 14 用于凈化的容器內(nèi)的真空過(guò)低 / 液體溫度的不穩(wěn)定 / / 缸向下運(yùn)動(dòng)的速度太低 / / / / / 氣體壓力太低 / / / / 計(jì)量 泵磨損嚴(yán)重 / / / 測(cè)量系統(tǒng)的機(jī)器精度過(guò)低 / / / / / 比例過(guò)大或過(guò)小 / / / / / 樹脂含有石英 / 說(shuō)明：“ ”的數(shù)量表示錯(cuò)誤及其原因的相對(duì)度 3 錯(cuò)誤診斷的模糊模型的結(jié)構(gòu) 我們確定 了 VRSDE 運(yùn)行狀態(tài)中的 各種故障 ，用 V 來(lái) 發(fā)表 ： 15 V1 比例浮動(dòng) V2 不充分混合 V3 不夠凈化 V = V4 = 噴射嘴泄露 V5 量子浮動(dòng) V6 量子減少 V7 沒(méi)有液體從噴射嘴噴出 而引起各種錯(cuò)誤癥狀的原 因，我們用 U 表示： U1 單方向閥故障狀態(tài) U2 閉閥故障狀態(tài) U3 噴射廳的液體泄露 U4 注射器內(nèi)的液體泄露 U5 用于凈化的容器內(nèi)的液位過(guò)低 U6 用于凈化的容器內(nèi)的真空過(guò)低 U = u7 = 液體溫度不穩(wěn)定 U8 缸向下運(yùn)動(dòng) 的速度太低 U9 氣體壓力太低 U10 計(jì)量 泵磨損嚴(yán)重 U11 測(cè)量系統(tǒng)的機(jī)器精度過(guò)低 U12 比例過(guò)大或過(guò)小 U13 樹脂含有石英 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(lái)表示 : r11 r12 . r17 R = r21 r22 . r27 . . . . r13.1 r13.2 r13.4 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 . 通過(guò)對(duì)傳感器數(shù)據(jù)的處理 ,我們可以得到癥狀模糊矩陣 : A:A=a1/u1+a2/u2+.+a13/u13=(a1,a2,.,a13) 0 ai 1 i=1,2,.,13 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 16 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 . 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 . B=A*R “ *”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 : B=b1/v1+b2/v2+.+b7/v7=(b1,b2,.b7) 公式中 0 =bj a4 矩陣 A 中 ,表示在注射廳內(nèi)泄露的元素 在注射廳內(nèi)的真空度和和管子接頭處液體壓力相差太大 (p) Down-Half-T 元素?cái)?shù)值 1 P a4= 1-e-k( P- ) Pi a6 矩陣 A 中 , 表示用于凈化的容器內(nèi)的真空度太低的元素 用于凈化的容器內(nèi)的真 空度 (Pi) Up-Half-Gauss 元素?cái)?shù)值 a6=max(a6i) 0 Pi a6i= 1-e-k(Pi-100)2 Pi 18 a8 矩陣 A 中 , 表示缸向下運(yùn)動(dòng)的速度太低的元素 缸向下運(yùn)動(dòng)的速度 (x) Down-Half-Trapezia 元素?cái)?shù)值 1 0 x a8= (r2-x)/(r2-r1) 1 2 a9 矩陣 A 中 , 表示氣體壓力過(guò)低的元素 氣體壓力 (P) Down-Half-Gauss 元素?cái)?shù)值 1 P a9= e-k(P-5)2 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 元素 解釋 元素的確定法則 a1 矩陣 A 中 ,表示單方向閥處于故障狀態(tài)的元素 單方向閥的運(yùn)動(dòng)精度 &0.05mm & 0.05mm a1=0.98 a1=0.00 兩個(gè)單方向罰的運(yùn)動(dòng)情況 t0.5s 0.1s0.05mm a2=0.96 & 0.05mm a2=0.00 閉閥關(guān)閉時(shí)的壓力 P4.8 c a7=0.95 2.6 c3.6 c a7=0.93 19 不穩(wěn)定時(shí) 1.9 cTb4.6 c a7=0.98 2.1 cTmix4.6 c a7=0.65 Tmix 2.1 c a7=0.00 a10 矩陣 A 中 ,表示計(jì)量泵磨損嚴(yán)重的元素 樹脂含有石英 a10=0.15 樹脂不含有石英 a10=0.00 a11 矩陣 A 中 ,表示測(cè)量系統(tǒng)的機(jī)器精度過(guò)低的元素 在 2 年內(nèi) VRSDE 的操作狀態(tài) a11=0.08 在 2-4 年 VRSDE 的操作狀態(tài) a11=0.13 超過(guò) 4 年 VRSDE 的操作狀態(tài) a11=0.25 a12 矩陣 A 中 ,表示比例過(guò)大或過(guò)小的元素 A:B=100:100 a12=0.08 A:B=100:80 a12=0.09 A:B=100:34 a12=0.34 a13 矩陣 A 中 ,表示樹脂中含有石英的元素 樹脂含有石英 a13=0.80 樹脂不含有石英 a13=0.00 3 4 錯(cuò)誤診斷原則的確定 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 1 和 2 ( 1 2),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 : 當(dāng) max (bj) 1時(shí) , VRSDE 處于正常狀態(tài) 20 當(dāng) 1 2時(shí) .VRSDE 處于故障狀態(tài) 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(lái)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 1 和 2的最大值如下 : 1 =0.50 2 =0.80 4 錯(cuò)誤自動(dòng)診斷實(shí)驗(yàn) 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 . 錯(cuò)誤自動(dòng)診斷系統(tǒng)的數(shù)學(xué)模型建立在無(wú)數(shù)次實(shí)驗(yàn)得來(lái)的經(jīng)驗(yàn)的基礎(chǔ)上 ,因此我們必須進(jìn)行實(shí)驗(yàn)來(lái)證實(shí)其準(zhǔn)確性 .實(shí)驗(yàn)的方法可以描述成如下 :通過(guò)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(lái)檢測(cè)錯(cuò)誤存在與否 ,這樣可以保證系統(tǒng)準(zhǔn)確無(wú)誤 .此外 ,