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.     

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.     

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.     

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.     

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.     

A New Method of Diesel Engine Fuel System Fault Diagnosis

A method of diesel engine fuel system fault diagnosis based on wavelet transform and fuzzy C-means clustering is presented. Five characteristic parameters of reflecting fault state are distilled with wavelet transform of pressure wave of high-pressure oil pipe of diesel. The theory and generic approach of fuzzy C-means clustering algorithm (FCM) is given, and the validity of evaluating fuzzy clustering making use of partition coefficient, partition entropy and parting coefficient is pointed out. Identification of fault mode can be completed utilizing standard fault character modes established by FCM algorithm, and calculating and comparing the similarity degree between this standard mode and sample. The arithmetic is applied to all kinds of typical faults diagnose in the diesel engine fuel system. Measuring results indicate that the precision of fault diagnosis is increased with the analysis of wavelet and FCM.     

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 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.     

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.     

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.     

Fault Section Diagnosis Algorithm of Distribution Networks Based On Topology Identification

An algorithm of fault section diagnosis based on topology identification for distribution networks is presented. By decomposing the topologic matrix which describing the distribution network into two parts,one part only contain the complex coupling factor ,and the other ignore the complex coupling factor. Using this method, the fault zones in distribution network can be identified and isolated efficiently, and the vertexes of the zones can also be identified automatically. This approach adapts to the changefully network structures.     

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.     

Application on Extracting Feature in State Monitoring and Fault Diagnosis

With the development and application of information and internet and virtual instrument technology ,the virtual globular company based on internet arises .To study remote state monitoring, remote fault detection and diagnosis about large scale, complicated and integrative equipment become very important. In the whole fault diagnosis system , the detecting ,data acquisition is original ,processing; transform and extracting features with the signal detected is a key factor. The theories and methods used in mechanical fault diagnosis is stated. The application of signal process and its feature extracting methods is introduced which are time domain, frequency domain and time frequency domain analysis, in state monitoring and fault diagnosis with its signal analysis. The processing method of random time variant special signal is given also.     

Application of Multi-agent Technology in Power System

Modern power system is a complex, open and distributed system. Multi-agent is a new technology of distributed artificial intelligence. It is easy to make the distributed systems run well. The basic principles and the applicable scopes of Multi-agent are described in this paper. Especially the various applications of Multi-agent in power system are introduced, such as security-defense system, secondary voltage control, electricity markets, EMS and power plant. The further studying works of Multi-agent are schemed. The the studying ways in control system, adaptive ability and foreground on wide control fields of power system. applications of the other fields in power system are described, intelligence, communications are pointed out and especially the foreground on wide control fields of power system.     

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.     

Analysis of the Modulation Phenomenon of Complex Vibration in Gearbox Fault Diagnosis

This paper presents a modulation phenomenon of complex vibration which occured when gear teeth broke or shaft seriously bent in gearbox. Based on the mechanism of gear vibration and modulation sideband,the peper analyzes this complex modulation phenomenon.