發(fā)布時間：2018-05-29 20:13

  本文選題：射頻指標 + 自動測試　； 參考：《南京理工大學》2017年碩士論文

【摘要】：隨著測試技術以及人工智能的發(fā)展,自動測試技術在逐步擺脫專業(yè)定制向模塊化、通用化發(fā)展的同時,診斷技術也進入了以知識處理為核心,信號處理、建模處理與知識處理相結合的智能診斷技術階段,自動測試與故障診斷的結合也一直是研究熱點之一。無線通訊設備研發(fā)階段射頻指標測試當前主要采用測試人員全程值守的半自動化測試方案,然后由測試專家人工完成后期的數(shù)據(jù)分析以及測試問題定位。上述工作的效率并不能完全滿足當前研發(fā)任務的需求。本課題針對測試及故障定位效率不足等問題,設計并實現(xiàn)了一種射頻指標自動測試及智能故障診斷系統(tǒng)。首先對ZigBee、Sub-GHz射頻指標測試需求進行了詳細分析,設計了一種基于虛擬儀器技術的自動測試方案,采用華為公司Impeller環(huán)境作為測試程序運行管理平臺。針對自動測試用例設計,采用測試套形式對測試儀器控制、被測設備指令輸入、文檔讀寫等通用代碼進行管理,使系統(tǒng)具有良好的兼容性和可移植性。然后針對峰值功率、頻譜模板、接收靈敏度等38個測試項的不同測試方法,分別詳細說明了測試程序的實現(xiàn)過程,給出了相應的程序流程圖。接著討論了當前理論上較為成熟的專家系統(tǒng)和BP神經(jīng)網(wǎng)絡在射頻指標測試問題智能定位方面的應用,設計并實現(xiàn)了一種以專家系統(tǒng)為主,BP神經(jīng)網(wǎng)絡為補充的智能故障診斷系統(tǒng)架構。采用面向?qū)ο蠹夹g和產(chǎn)生式規(guī)則結合的知識表示方法進行系統(tǒng)知識庫設計,并使用MySQL數(shù)據(jù)庫建立和維護知識庫;采用基于知識的專家系統(tǒng)作為診斷推理核心,當故障信息與顯式知識庫無法匹配時,系統(tǒng)將調(diào)用基于數(shù)值計算的神經(jīng)網(wǎng)絡進行推理;基于預置文本法設計了解釋機制,可以在診斷結果輸出的同時,對診斷結果及推理過程做必要的解釋;設計并實現(xiàn)了WEB化交互界面,在保證系統(tǒng)診斷性能及穩(wěn)定運行的前提下,實現(xiàn)了友好的人機交互方式。本系統(tǒng)已經(jīng)在華為企業(yè)網(wǎng)絡硬件實驗室(南京)投入運行,顯著提升了射頻指標測試效率,同時也較好實現(xiàn)了測試問題的智能定位,滿足了設計性能需求。
[Abstract]:With the development of testing technology and artificial intelligence, automatic test technology has gradually moved away from professional customization to modularization, and at the same time, diagnosis technology has also entered the core of knowledge processing and signal processing. The combination of automatic testing and fault diagnosis has been one of the hotspots in the intelligent diagnosis technology stage which combines modeling and knowledge processing. At present, the radio frequency index test of wireless communication equipment development stage mainly adopts the semi-automatic test scheme which the testers are on duty in the whole process, and then the later data analysis and test problem orientation are manually completed by the test experts. The efficiency of the above work does not fully meet the needs of the current R & D task. In order to solve the problem of low efficiency of test and fault location, an automatic testing and intelligent fault diagnosis system for RF index is designed and implemented in this paper. Firstly, the requirements of ZigBeeg Sub-GHz radio frequency index testing are analyzed in detail, and an automatic test scheme based on virtual instrument technology is designed. Huawei's Impeller environment is used as the running management platform of the test program. For the design of automatic test cases, the test suite is used to manage the general code such as the control of test instrument, the input of instruction, the reading and writing of documents, etc., which makes the system have good compatibility and portability. According to the different testing methods of 38 test items, such as peak power, spectrum template and receiving sensitivity, the implementation process of the test program is explained in detail, and the corresponding program flow chart is given. Then it discusses the application of expert system and BP neural network, which are mature in theory, in the intelligent localization of radiofrequency index testing problem. An intelligent fault diagnosis system architecture supplemented by expert system and BP neural network is designed and implemented. The knowledge base is designed by using object-oriented technology and knowledge representation method of production rule, and the knowledge base is established and maintained by MySQL database, and the expert system based on knowledge is used as the core of diagnosis reasoning. When the fault information and the explicit knowledge base can not match, the system will call the neural network based on the numerical calculation for reasoning, and design the explanation mechanism based on the preset text method, which can output the diagnosis results at the same time. The WEB interactive interface is designed and implemented, and the friendly man-machine interaction mode is realized on the premise of ensuring the diagnostic performance and stable operation of the system. The system has been put into operation in the Huawei Enterprise Network hardware Laboratory (Nanjing), which has greatly improved the efficiency of RF index testing, at the same time, it has realized the intelligent positioning of the test problem and satisfied the requirement of design performance.
【學位授予單位】：南京理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP274;TP277

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