英文翻譯格式

1、英文資料翻譯 系 別軟件與服務外包學院.專 業(yè) 通信網(wǎng)絡與設備 .班 級 通信0901 .學生姓名 韓麗司 .學 號 090969 .指導教師 陳佳 .二一二年二月8Based on the data fusion of intelligent fault diagnosis system1. Primed wordsMulti sensor data fusion technology was initially mostly used in the military field, but the computer, network and communication technology

2、the rapid development that the application range is expanded greatly. In recent years, many scholars of the data fusion rules and strategy theory to conduct extensive research and improvement. While the artificial intelligence technology research makes the data fusion to improve the knowledge of tar

3、get decision height, the auxiliary function is greatly strengthened. At the same time, with the industrial technology make a spurt of progress, an intelligent fault diagnosis system of demand in quantity and quality greatly improved. As the intelligent fault diagnosis system for the most basic, the

4、most effective information processing tools, multi sensor data fusion technology development will promote the progress of intelligent fault diagnosis system.2. Multi sensor data fusion and improved D - S theoryFrom a military application perspective, data fusion is to make full use of different time

5、 and space of the multi sensor information resources according to the time sequence, using computer technology to obtain multiple sensor observation information in certain criteria to be automatic, integrated analysis, control and use, access to the object consistency of interpretation and descripti

6、on, to complete required decision-making and estimation tasks, allowing the system to obtain than its components the better performance of H3. The author uses the present generally agree that the pixel level, feature layer and decision layer three layer fusion structure. The decision level fusion ta

7、rget is to achieve the target situation diagnosis and assessment, applied to the main Bayesian probability reasoning and D S evidence theory. The data fusion method to solve the uncertain information processing problems, D - S method with Dempster - Shafer evidential theory as a foundation, its core

8、 is Dempster synthesis rules, for uncertain information expression and synthesis provides natural and robust method. Will force S evidence theory is used for multi sensor fusion, obtained from the sensor related value is the theory of evidence, it can constitute the targets to be recognized patterns

9、 of belief function assignment, that each target model hypothesis of credibility, each sensor consists of an evidence of group.Multi sensor data fusion is through D S united rules to combine several evidence group to form a new integrated evidence group, called the D S association rules with each se

10、nsor of confidence function distribution formed by fusion of confidence function distribution, which is target mode decision-making provide comprehensive and accurate information of n . In practical application, D - S method requires evidence of independence and evidence combination rule theory supp

11、ort, and the calculation of potential exists the problem of combinatorial explosion, so only the single fusion methods are difficult to obtain ideal fusion effect.D - S evidence theory has does not require a priori probability advantages; expert system has a problem domain knowledge; fuzzy system ha

12、s higher fuzzy language processing; high order neural network has the capacity to be big, approximation ability, fault-tolerant a wide range of features, so the D s evidence theory fusion method with multiple division complementary to improve the D - s method, improve the fusion system for target id

13、entification accuracy and reliability, which make the system has strong self learning ability and ability to adapt to their environment.3. Intelligent fault diagnosis systemDiagnosis system of a failure mode is often caused by multiple fault symptom, and a fault symptom can be caused by multiple fai

14、lure modes, is many-to-many form. So without a sensor to ensure that at any time to provide complete and reliable information, it is usually in multiple sensor based on integrated diagnosis. In essence, fault diagnosis system is the use of diagnostic object system runs a variety of state information

15、 and various kinds of existing knowledge, information processing, finally get on the system operation condition and fault condition of the comprehensive evaluation of n3. Data fusion is typical application system is C3I system, especially in multiple target tracking system. According to C3I system,

16、fault diagnosis information required for access to more diverse, describe diagnostic mathematical model of the object may be greater than the space coordinates and velocity characteristics are more complex, the fault diagnostic object link between ( coupling, backup, transfer ) can be tracked object

17、 of coordinated action of the relations to be more close, but can make the diagnosis object is regarded as a sensor through the systematic observation of the particular state space, the fault signal is the space in the specific target signal, the fault diagnosis is based on the signal and the knowle

18、dge base to determine the fault alarm.The fault diagnosis system, very suitable for using the previously described multisensor fusion structure, a pixel layer is layer of data fusion for sensor reflect the direct data; feature layer corresponding to various fault diagnosis methods of data fusion, th

19、e results are effective decision; decision fusion for integrated subsystems via the fusion rule of combination made the final the results of fault diagnosis and troubleshooting. The three layer structure corresponding to the fault diagnosis system of monitoring, diagnosis and decision function. In f

20、ault diagnosis systemData fusion in the certain degree can make the system to obtain the accurate state estimation, increase the degree of confidence, to reduce ambiguity, improve diagnostic performance, improve the multi sensor information resources utilization. But with the development of new tech

21、nology, fault diagnosis system is gradually introduced into artificial intelligence technology, the main performance is: the use of neural network local diagnosis; the use of multiple concurrent ES using multiple knowledge in the field of synthetic information; the use of advanced database managemen

22、t technology for decision support system using reasoning; learning, so the automatic adapt to all kinds of trend. In addition, on the basis of data fusion, the fusion levels increase, the data mining and knowledge ( including rules, method and model) fusion.4Based on the data fusion of intelligent d

23、iagnosis systemFrom the perspective of multi sensor data fusion, typical application example is the process monitoring and fault diagnosis, and from the perspective of intelligent fault diagnosis system, usually in multi sensor data fusion based on integrated diagnosis. Based on the above on the mul

24、ti-sensor data fusion technology and intelligent fault diagnosis system are discussed, the following two techniques for organic coupling, based on the establishment of a multi sensor data fusion of intelligent fault diagnosis system structure frame.4.1 Working principleThe system is composed of inpu

25、t output system, a sensor signal acquisition system, signal pre-processing system, expert system and decision fusion system. When the system works, the first use of multi sensor signal acquisition and signal data were preprocessed ( such as signal filtering, spectrum analysis, wavelet analysis, etc.

26、 ) will be processed information and diagnostic system of expert knowledge base ( rules, methods and models of knowledge) according to certain rules, and then each sub-system is the local diagnosis results are parallel fusion for decision fusion system for global diagnosis, the final output diagnosi

27、s results and relevant information will be stored in the database and knowledge base for the use of data mining technology for knowledge discovery for the necessary data on reserves.4.2 Key technology4.2.1 Local diagnosis systemNeural network can realize the complex nonlinear mapping, in the field o

28、f fault diagnosis has been widely used export . When the system parameters for the diagnosis of more, signs of the large amount of information times, due to the inevitable contradiction between sample and random, if the high dimensional symptom information input at the same time to the same network

29、processing, will make the long training time, the diagnosis of poor results, sometimes evenTo cause the network convergence. Therefore, the human brain in different regions with different information. Different signals are also by the respective neural network diagnosis. So the high dimensional symp

30、tom space decomposition into low dimensional symptom space, the process may also be referred to as the local diagnosis. In addition, the neural network system can effectively solve the expert system part of the limitations, so the use of the neural network expert system.4.2.2 Decision fusionUsing ne

31、ural network for local diagnosis, from each or several diagnostic parameters can get their diagnostic results, each subsystem is responsible for a fault diagnosis, from different angles, fault diagnosis, decision fusion of these diagnostic results fusion, makes the subsystem is formed between the co

32、nsultation, utmost to improve the diagnosis rate. For preprocessing information fusion, inference is more important than numerical computation, should be based on knowledge of the technology of expert system and D - S theory of evidence combination method of fusion.4.2.3 Data mining and knowledge fu

33、sionSystem existing operating state to revise the original system knowledge base, can be more quickly, more accurate, more comprehensive fault diagnosis, this is the data mining and knowledge integration issues, data mining techniques in information fusion system will become the necessary part of.5.

34、 The endMulti sensor data fusion technology and intelligent fault diagnosis system is very practical, and the organic integration of the two can on their respective technology development to promote each others role. But at present the information fusion system specific fusion rule method based on k

35、nowledge fusion technology is still not mature, also remains to be improved, the intelligent diagnosis system need to be improved for AI Technology application. But I believe that with all the technology and the gradual improvement of the practice, continue to accumulate experience, based on the dat

36、a fusion of intelligent fault diagnosis system will be developed faster and wider application.基于多傳感器數(shù)據(jù)融合的智能故障診斷系統(tǒng)1引 言多傳感器數(shù)據(jù)融合技術最初大多應用于軍事領域，但計算機、網(wǎng)絡以及通信等先進技術的飛速發(fā)展使它的應用范圍得到了很大的拓展。近年來，眾多學者對數(shù)據(jù)融合的規(guī)則與策略的理論進行了廣泛的研究和改進。而人工智能等技術的研究使得數(shù)據(jù)融合提升到了知識融合的高度，對目標決策的輔助作用大大加強。與此同時，隨著工業(yè)技術的突飛猛進，智能故障診斷系統(tǒng)的需求在數(shù)量上和質(zhì)量上大大提高了。作為智能

37、故障診斷系統(tǒng)中的最基本、最有效的信息處理工具，多傳感器數(shù)據(jù)融合技術的發(fā)展將推動智能故障診斷系統(tǒng)的進步。2多傳感器數(shù)據(jù)融合與改進DS理論從非軍事應用的角度來說，數(shù)據(jù)融合是指充分利用不同時間與空間的多傳感器信息資源，采用計算機技術對按時序獲得的多傳感器觀測信息在一定準則下加以自動分析、綜合、支配和使用，獲得對被測對象的一致性解釋與描述，以完成所需的決策和估計任務，使系統(tǒng)獲得比它的各組成部分更優(yōu)越的性能H3。筆者采用目前普遍認同的像素層、特征層以及決策層的三層融合結構。其中決策級融合的目標是實現(xiàn)對目標態(tài)勢的診斷和評估，應用到的主要有貝葉斯概率推理和DS證據(jù)理論等方法。這些數(shù)據(jù)融合方法都必須解決對不確

38、定信息的處理問題，DS方法以DempsterShafer證據(jù)理論為基礎，其核心是Dempster合成規(guī)則，為不確定信息的表達和合成提供了自然而強有力的方法。將脅S證據(jù)理論用于多傳感器融合時，從傳感器獲得的相關數(shù)值就是該理論中的證據(jù)，它可構成待識別目標模式的信度函數(shù)分配，表示每一個目標模式假設的可信程度，每一傳感器構成一個證據(jù)組。所謂多傳感器數(shù)據(jù)融合就是通過DS聯(lián)合規(guī)則聯(lián)合幾個證據(jù)組形成一個新的綜合的證據(jù)組，即用DS聯(lián)合規(guī)則聯(lián)合每個傳感器的信度函數(shù)分配形成融合的信度函數(shù)分配，從而為目標模式的決策提供綜合準確的信息n。實際應用中，DS方法要求證據(jù)的獨立性和證據(jù)合成規(guī)則的理論支持，而且計算量存在著潛

39、在的組合爆炸問題，所以僅靠這種單一的融合方法難以獲得理想的融合效果。DS證據(jù)理論具有不需要先驗概率的優(yōu)點；專家系統(tǒng)具有問題領域的豐富知識；模糊系統(tǒng)具有較高的模糊語言處理能力；高階神經(jīng)網(wǎng)絡具有容量大、逼近能力強、容錯范圍廣的特點，所以將Ds證據(jù)理論與多種融合方法的分工互補能夠改進Ds方法的不足，提高融合系統(tǒng)中的目標識別的精確性和可靠性，使得系統(tǒng)具有較強的自學習能力以及對外界環(huán)境的適應能力。3智能故障診斷系統(tǒng)被診斷系統(tǒng)的一個故障模式往往引起多個故障征兆，而一個故障征兆又可以由多種故障模式引起，是多對多的形式。所以沒有一種傳感器能夠保證在任何時候提供完全可靠的信息，因此通常都是在多傳感器的基礎上進行

40、綜合診斷。本質(zhì)上，故障診斷系統(tǒng)是利用診斷對象系統(tǒng)運行的各種狀態(tài)信息和已有的各種知識，進行信息的綜合處理，最終得到關于系統(tǒng)運行狀況和故障狀況的綜合評價n3。數(shù)據(jù)融合現(xiàn)在應用的典型系統(tǒng)是C3I系統(tǒng)，尤其是多目標跟蹤系統(tǒng)。比照C 3I系統(tǒng)，故障診斷所需信息的獲取途徑要更加多樣，描述診斷對象的數(shù)學模型可能比空間中坐標和速率等特征要更加復雜，診斷對象的故障之間的聯(lián)系(耦合、備份、傳遞等)可能要比跟蹤對象之問協(xié)調(diào)行動的關系要更加緊密，但可以把診斷對象看做是一個通過傳感器系統(tǒng)觀測的特定狀態(tài)空間，其故障信號就是該空間中的特定目標信號，故障診斷就是根據(jù)信號和知識庫確定故障報警。對于故障診斷系統(tǒng)來講，很適合采用前面介紹的多傳感器融合結構，像素層也就是數(shù)據(jù)層的融合針對傳感器反映的直接數(shù)據(jù)；特征層對應各種故障診斷方法，對數(shù)據(jù)融合的結果進行有效的決策；決策層融合綜合各個子系統(tǒng)通過融合組合規(guī)則做出最終的故障診斷結果和故障對策。這三層結構分別對應于故障診斷系統(tǒng)的監(jiān)