Topology Identification of Distribution Networks Based on Hierarchical Model

Traditional model of distribution networks mostly research the response of input excitation, by describing elements and connections of elements for distribution networks. When the states of vertexes in distribution networks have changed, the connections of elements transformation. Owning to the mutual couplings and branches of distribution networks, it is hard to achieve the global optimization for load balancing, reconfiguration and restoration after fault by nomograph. A new model based on hierarchical model is presented. This model can be employed to identify the zone and branches efficiently and offer a cogent topological analysis method for the global optimization. The test result shows the effectiveness of the proposed method.     

Application of Serial Ports Simulation in a Non-communication Fault Detection Device of Distribution Networks

This paper summarizes two kinds of fault detection techniques for distribution automation, that is with and without communication in fault detection, and then analyzes their merits and shortcomings. Moreover, according to the development tendency of distribution automation techniques, this paper presents an approach, through which the noncommunication FTU devices can detect transient fault voltage of distribution systems effectually. To detect transient fault voltage without standby batteries, the PIC Single Chip microcomputer with low power consumption and DSP chip with powerful data processing capabilities are adopted to form a dual CPU system. To realize the communication between the two CPUs, the general I/O port,s of the DSP chip are firstly used to simulate the UART communication of the serial ports and communicate with the PIC Single Chip microcomputer. The novel FTU devices based on this approach are tested in static and dynamic simulation experiments, the results are satisfied.     

Fault Diagnosis in Hydraulic Turbine Governor Based on BP Artificial Neural Network

The governing system of hydraulic turbine generator plays an important role in power system. It is significant to find out the faults of governing system and remove them quickly. This paper sets up a new fault diagnosis model of the hydraulic turbine generator governing system with the advanced ANN (artificial neural net). This 17-in-13-out model consists of three layers. It is proved that this model can find the fault accurately.     

The Application of Fuzzy Neural Networks for Power Transformer Fault Diagnosis

This paper presents the application of fuzzy neural networks (FNN) in power transformer faults diagnosis.A FNN model is builded,in which input is transformer oil color spectrum analysis and output is fault type.The test results of fault examples show its effectiveness and potential applicable worthiness.     

Study on the Method of Intelligence Fault Diagnosis System for Drill

It is very difficult to simulate the motion process of drill by accurate mathematics model for the complexity and invisibility of stratum. The fault of drill is usually identified and disposed by personal experience so far. This means it can not estimate the trend of the equipment running good or not and the reason conduced the fault or location and degree of fault by data measured. Farther, it can not give the expert suggestions. Based on the studying of drill fault, artificial intelligence and expert system have been used in the petroleum drilling engineering, the theory and method of fault diagnosis intelligence system for drill have been studied. It also constitutes the expert knowledge database of graphic Fuzzy Neural Network for the familiar fault of drill. The intelligence reasoning machine which consists of expert rule, Fuzzy logic and artificial Neural Network have been bring forward. And the fault diagnosis system it makes for drill can be applied in complex system which includes multi-variable, multi-parameter and multi-process.     

Knowledge Representation for Rotative Machine Fault Diagnosis Based on Semantic Net

Aiming at knowledge representation problem of expert system for rotary machinery fault diagnosis, semantic net knowledge representation is discussed. Semantic net to represent expert knowledge of rotary machinery fault diagnosis is explored. Its realizing method is afforded in computer language, in addition, demostrating model is also developed in Visual C++. The result shows good validity of the knowledge representation.     

A Three Valued Model of System Level Fault Diagnosis

We propose a three valued model of system level fault diagonsis,define a class of diagnosable systems,give a characterization of them,and study their optimal design.     

A Multi-agent Fault Diagnosis System of Rotating Machinery Based on CBR

Recently,distributed intelligent systems based on multi-agent have been applied to many fields successfully.the multi-agent technique is introduced into the rotating machinery fault diagnosis system.With the combination of CBR technique,Basalstructure and development approach of the system are analyzed,and better cooperation among agents is realized.As a result,the system overcomes the limitation of single fault diagnosis method,and solves the contradictionbetween versatilityand adaptabilityof diagnosis software.Furthermore,a reference can be used for the research and development of multi-algorithmdiagnosis system of rotating machinery.The application in a factory's Networked Online Monitoring and Fault Diagnosis System for Turbine Fan shows that the system can diagnosefault rapidly and exactly.     

The Development of Intelligent Fault Diagnosis System Based KBE Graph

Aimed at solving the insufficiency problem in the current intelligent fault diagnosis system, such as lack of development tools, inference oversimplification, and developed a kind of diagnosis system based on network and KBE graph. The system is simple and easy to establish, modify, expand and maintain and does not need further coding. In addition, it can significantly save time and reduce the chance of failure. The integration of expert rules, fuzzy logic and nerve network enables the system to adapt to those complicated systems which involve in multi-variable, multi-parameter, multi-object and multi-process. As an example, an on-line detection system for simulate-on rotor test bench has been established and typical faults were successfully diagnosed through it, which proved the validity and reliability of the system. Furthermore, the system has been declared national invention patent.     

Diagnosis Model Based Neutral-network in Galactophore Cancer Cell Identification

To the classified problem of the Galactophore Cancer Cell Identification, the paper researched the classified mechanism and optimized parameter for more-layer radial basis function (RBF) network, adopted the method of gradient descent with momentum and adaptive learned backpropagation, constructed the two-model of the Galactophore Cancer Cell Identification based on BP Neural-Network and RBF Network. Classified principle based on RBF Neural network was also discussed, and the method for data processing was studied. RBF network has more advantage, such as fault tolerance, nonlinear mapping, etc.. Experiment results show that, on the model based RBF Neural Network, performance is steady, training time is short and classified results are good.     

Analysis of Information Flow Based on Entropy of Fault Diagnosis

Under the pointview of epistemology and generalized information theory, fault diagnosis can be regarded as the state identification process by dint of various cognitive tools. Therefore, information Entropy can be employed to explain the transfer of information during identifying the faults. The mathematical deduction of information Entropy is given and the idea of Multi-Symtom Domains Comprehensive Feature Knowledge to solve fault diagnosis problems caused by insufficient knowledge and lower cognitiving ability for complex knowledge system is presented.     

Fault Diagnosis Expert System of Combustion Engine with Rough Set Theory

On the basis of fault diagnosis expert system, the rough set theory is introduced. Knowledge representation system table is taken as a major tool to reduce the rules of expert system in which unnecessary properties are eliminated. The redundancy of fault diagnosis information is revealed. The complexity of fault diagnosis expert system's structure is also reduced. The decision making rules are given finally.     

A New Adaptive Sequential Diagnosis Algorithm in the PMC Model

We present an adaptive algorithm for sequential diagnosis in the PMC model,which can directly identify a faulty processor without first identifying a fault-free processor.     

State and Prospects of Diesel Engine Fault Diagnosis Technique

As a kind of complex power machinery, diesel engine is being paid more and more attention to its condition monitoring and fault diagnosis technology. In the fault diagnosis field of diesel engine, the technique of signal process, character abstraction and identification method have formed a system, but there is a certain distance away from practical. This paper analyzes the common faults and influencing factor of diesel engine. The principle, characteristic and disadvantage of modern fault diagnosis technology, such as various time-frequency methods based on vibration signal, speed fluctuation method, iron content and spectrometry, grey system theoretical diagnosis method, artificial neural network and expert system fault diagnosis method, were reviewed. The difficulties and the development direction of diesel engine fault diagnosis were put forward in the end.     

Artificial Neural Network Fault Diagnosis System Based on Rough Set Theory

On the basis of fault diagnosis neural network model, in this paper, knowledge representation system of rough set theory is taken as a major tool to delaminate the complex neural network and in which unnecessary properties are eliminated. This method overcomes some shortcomings, such as network scale is too large and the rate of classification is slow. The good effect that reduces the matching quantity of pattern search in classification course is gotten. The structure and algorithm of layered-mining neural network model based on rough set theory are also given. The example shows that this system has higher reference value in practical application.     

A Design of RBF Neural Networks Based on Genetic Algorithms

Genetic algorithms are proposed to automatically configure RBF neural networks.The network configuration is formed as a subset selection problem.Using new genetic operators,the genetic algorithms are improved in convergence and applicatility.The configuration and optimal principle of genetic algorithms are introduced.The method of generating the RBF networks is given.Finally,the simulated result on practical data is used to demonstrate the performance of the new algorithms.     

Research and Application of Information Fusion Technology on Machinery Fault Diagnosis

Aiming at insufficiency exists in the process of machinery fault diagnosis at present, the paper takes consider of the application of information fusion technology in fault diagnosis to make fault diagnosis effectively. The applications of information fusion methods based on neural network, the Bayesian theory and the D-S theory are discussed in detail. Example is also given to explain the validity of information fusion technology in diagnosis analysis.