英文翻译格式

1、英文资料翻译 系 别软件与服务外包学院.专 业 通信网络与设备 .班 级 通信0901 .学生姓名 韩丽司 .学 号 090969 .指导教师 陈佳 .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.基于多传感器数据融合的智能故障诊断系统1引 言多传感器数据融合技术最初大多应用于军事领域，但计算机、网络以及通信等先进技术的飞速发展使它的应用范围得到了很大的拓展。近年来，众多学者对数据融合的规则与策略的理论进行了广泛的研究和改进。而人工智能等技术的研究使得数据融合提升到了知识融合的高度，对目标决策的辅助作用大大加强。与此同时，随着工业技术的突飞猛进，智能故障诊断系统的需求在数量上和质量上大大提高了。作为智能

37、故障诊断系统中的最基本、最有效的信息处理工具，多传感器数据融合技术的发展将推动智能故障诊断系统的进步。2多传感器数据融合与改进DS理论从非军事应用的角度来说，数据融合是指充分利用不同时间与空间的多传感器信息资源，采用计算机技术对按时序获得的多传感器观测信息在一定准则下加以自动分析、综合、支配和使用，获得对被测对象的一致性解释与描述，以完成所需的决策和估计任务，使系统获得比它的各组成部分更优越的性能H3。笔者采用目前普遍认同的像素层、特征层以及决策层的三层融合结构。其中决策级融合的目标是实现对目标态势的诊断和评估，应用到的主要有贝叶斯概率推理和DS证据理论等方法。这些数据融合方法都必须解决对不确

38、定信息的处理问题，DS方法以DempsterShafer证据理论为基础，其核心是Dempster合成规则，为不确定信息的表达和合成提供了自然而强有力的方法。将胁S证据理论用于多传感器融合时，从传感器获得的相关数值就是该理论中的证据，它可构成待识别目标模式的信度函数分配，表示每一个目标模式假设的可信程度，每一传感器构成一个证据组。所谓多传感器数据融合就是通过DS联合规则联合几个证据组形成一个新的综合的证据组，即用DS联合规则联合每个传感器的信度函数分配形成融合的信度函数分配，从而为目标模式的决策提供综合准确的信息n。实际应用中，DS方法要求证据的独立性和证据合成规则的理论支持，而且计算量存在着潜

39、在的组合爆炸问题，所以仅靠这种单一的融合方法难以获得理想的融合效果。DS证据理论具有不需要先验概率的优点；专家系统具有问题领域的丰富知识；模糊系统具有较高的模糊语言处理能力；高阶神经网络具有容量大、逼近能力强、容错范围广的特点，所以将Ds证据理论与多种融合方法的分工互补能够改进Ds方法的不足，提高融合系统中的目标识别的精确性和可靠性，使得系统具有较强的自学习能力以及对外界环境的适应能力。3智能故障诊断系统被诊断系统的一个故障模式往往引起多个故障征兆，而一个故障征兆又可以由多种故障模式引起，是多对多的形式。所以没有一种传感器能够保证在任何时候提供完全可靠的信息，因此通常都是在多传感器的基础上进行

40、综合诊断。本质上，故障诊断系统是利用诊断对象系统运行的各种状态信息和已有的各种知识，进行信息的综合处理，最终得到关于系统运行状况和故障状况的综合评价n3。数据融合现在应用的典型系统是C3I系统，尤其是多目标跟踪系统。比照C 3I系统，故障诊断所需信息的获取途径要更加多样，描述诊断对象的数学模型可能比空间中坐标和速率等特征要更加复杂，诊断对象的故障之间的联系(耦合、备份、传递等)可能要比跟踪对象之问协调行动的关系要更加紧密，但可以把诊断对象看做是一个通过传感器系统观测的特定状态空间，其故障信号就是该空间中的特定目标信号，故障诊断就是根据信号和知识库确定故障报警。对于故障诊断系统来讲，很适合采用前面介绍的多传感器融合结构，像素层也就是数据层的融合针对传感器反映的直接数据；特征层对应各种故障诊断方法，对数据融合的结果进行有效的决策；决策层融合综合各个子系统通过融合组合规则做出最终的故障诊断结果和故障对策。这三层结构分别对应于故障诊断系统的监