發(fā)布時(shí)間：2018-04-05 07:17

  本文選題：節(jié)點(diǎn)測(cè)試設(shè)備　切入點(diǎn)：分布式測(cè)試系統(tǒng)　出處：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：隨著電子信息系統(tǒng)復(fù)雜化、大型化、網(wǎng)絡(luò)化、智能化程度的不斷提高,電子信息系統(tǒng)的測(cè)試設(shè)備朝著“分布式采集,集中化分析管理,共享數(shù)據(jù)資源”的分布式測(cè)試系統(tǒng)的方向發(fā)展。以往的分布式測(cè)試系統(tǒng)的節(jié)點(diǎn)測(cè)試設(shè)備只負(fù)責(zé)狀態(tài)監(jiān)測(cè)、往往不具備故障診斷和故障預(yù)測(cè)功能,并且通用性差,需針對(duì)測(cè)試節(jié)點(diǎn)的測(cè)試需求開(kāi)發(fā)專(zhuān)用設(shè)備,測(cè)試成本高,維護(hù)困難。針對(duì)這一問(wèn)題,本課題研制一種具有一定通用性、信息交互能力、故障診斷能力和故障預(yù)測(cè)能力的節(jié)點(diǎn)測(cè)試設(shè)備,以構(gòu)建信息化分布式的測(cè)試系統(tǒng),提高對(duì)復(fù)雜電子信息系統(tǒng)的測(cè)試能力和故障診斷、預(yù)測(cè)能力。在硬件設(shè)計(jì)上,選用高集成度、端子可復(fù)用的儀器模塊滿(mǎn)足被測(cè)節(jié)點(diǎn)測(cè)試信號(hào)的測(cè)試需求,利用儀器模塊的端子配置功能和超寬測(cè)量輸入替代TUA(Test Unit Adapter,測(cè)試接口適配器)的信號(hào)分配與調(diào)理功能,取消了專(zhuān)用的TUA,實(shí)現(xiàn)了節(jié)點(diǎn)測(cè)試設(shè)備的互換性、通用性和設(shè)備的小型化。另外,通過(guò)選用便攜式的PXI機(jī)箱,進(jìn)一步減小了節(jié)點(diǎn)測(cè)試設(shè)備的體積。針對(duì)電子信息系統(tǒng)故障原因與癥狀的隨機(jī)性與不確定性、故障樣本少、狀態(tài)信息有限和先驗(yàn)消息多源異類(lèi)等特點(diǎn),本課題在改進(jìn)DS(Dempster-Shafer)證據(jù)理論處理沖突證據(jù)不當(dāng)問(wèn)題的基礎(chǔ)上,提出了基于灰色關(guān)聯(lián)分析與改進(jìn)DS證據(jù)推理的故障診斷方法。該方法結(jié)合了灰色關(guān)聯(lián)分析處理“小樣本、貧信息、不確定性”問(wèn)題的優(yōu)點(diǎn)與DS證據(jù)理論在信息融合中考慮不確定性的優(yōu)點(diǎn)。針對(duì)大功率電源的電壓和光纖陀螺的隨機(jī)漂移誤差均隨時(shí)間呈趨勢(shì)變化的特點(diǎn),提出了基于ARMA-Elman神經(jīng)網(wǎng)絡(luò)的故障預(yù)測(cè)方法,該方法結(jié)合了ARMA模型對(duì)于線(xiàn)性時(shí)間序列的擬合能力與Elman神經(jīng)網(wǎng)絡(luò)對(duì)非線(xiàn)性時(shí)間序列的映射能力。提出的故障診斷算法與故障預(yù)測(cè)算法均以組件的形式實(shí)現(xiàn),供節(jié)點(diǎn)測(cè)試設(shè)備調(diào)用。測(cè)試軟件基于本單位的聯(lián)合試驗(yàn)平臺(tái)開(kāi)發(fā),通過(guò)開(kāi)發(fā)具有基本功能的組件構(gòu)建試驗(yàn)方案實(shí)現(xiàn)測(cè)試軟件的功能�；诒締挝宦�(lián)合試驗(yàn)平臺(tái)提供的信息交互服務(wù),實(shí)現(xiàn)各節(jié)點(diǎn)測(cè)試設(shè)備間相互訪(fǎng)問(wèn)、共享數(shù)據(jù)和相互調(diào)用。在故障診斷和故障預(yù)測(cè)組件的開(kāi)發(fā)工作中,通過(guò)構(gòu)建組件調(diào)用Matlab引擎的框架,方便將來(lái)擴(kuò)充更多的算法以提高組件故障診斷和故障預(yù)測(cè)的能力。最后,完成了軟硬件的系統(tǒng)集成測(cè)試工作,通過(guò)虛擬被測(cè)對(duì)象(Unit Under Test,UUT)和實(shí)物信號(hào)源對(duì)設(shè)備具有的信息化能力、遠(yuǎn)程測(cè)試能力、故障診斷能力和故障預(yù)測(cè)能力進(jìn)行驗(yàn)證,測(cè)試結(jié)果表明本課題研制的節(jié)點(diǎn)測(cè)試設(shè)備能夠滿(mǎn)足研制要求。
[Abstract]:As the electronic information system becomes more and more complex, large-scale, networked and intelligent, the test equipment of the electronic information system is oriented to "distributed collection, centralized analysis and management,"The development of distributed test system based on sharing data resources.In the past, the node test equipment of the distributed test system was only responsible for state monitoring, and often did not have the function of fault diagnosis and fault prediction, and the generality was poor. Therefore, special equipment should be developed to meet the test requirements of the test nodes, and the test cost was high.Maintenance is difficult.In order to solve this problem, this paper develops a kind of node test equipment which has certain generality, ability of information exchange, ability of fault diagnosis and ability of fault prediction, in order to construct an information distributed test system.Improve the test ability, fault diagnosis and prediction ability of complex electronic information system.In the hardware design, the instrument module with high integration and reusable terminal is selected to meet the test requirements of the test signal of the node under test.By using the terminal configuration function of the instrument module and the signal assignment and conditioning function of the ultra-wide measurement input instead of the TUA(Test Unit Adapter (test interface adapter), the special TUAs are eliminated, and the interchangeability, versatility and miniaturization of the node test equipment are realized.In addition, the volume of the node test equipment is further reduced by selecting the portable PXI chassis.Aiming at the randomness and uncertainty of fault causes and symptoms of electronic information system, the small number of fault samples, the limited state information and the multi-source heterogeneity of prior messages, this subject is based on the improvement of DSN Dempster-Shafer evidence theory to deal with the problem of improper conflict evidence.A fault diagnosis method based on grey correlation analysis and improved DS evidence reasoning is proposed.This method combines the advantages of "small sample, poor information, uncertainty" problem in grey relational analysis and the advantage of considering uncertainty in information fusion based on DS evidence theory.In view of the fact that the voltage of high power supply and the random drift error of fiber optic gyroscope are changing with time, a fault prediction method based on ARMA-Elman neural network is proposed.This method combines the fitting ability of ARMA model for linear time series and the mapping ability of Elman neural network to nonlinear time series.Both the fault diagnosis algorithm and the fault prediction algorithm are implemented in the form of components, which can be called by the node test equipment.The test software is developed based on the joint test platform of our unit, and the function of the test software is realized by developing components with basic functions to build the test scheme.Based on the information interactive service provided by the joint test platform, the test equipments of each node can access each other, share data and call each other.In the development of fault diagnosis and fault prediction components, it is convenient to expand more algorithms to improve the ability of component fault diagnosis and fault prediction by constructing the framework of component calling Matlab engine.Finally, the system integration test of hardware and software is completed, and the information ability, remote test ability, fault diagnosis ability and fault prediction ability of the equipment are verified by virtual object unit Under Test UUTU and physical signal source.The test results show that the node test equipment developed in this paper can meet the requirements of the development.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP277

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