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.     

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.     

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.     

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.     

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.     

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.     

Information Fusion Algorithm Based Evidence Combination

This paper applies the evidence combination theory to fuse the information of multi-neural network classifier. In order to make each classifier approach the ideal state, the heredity algorithm is applied to train it. The different capacity of each classifier is caused by different classified feature. Input feature can't be identified by one classifier and may be identified by another, Model identification can be performed by multi-classifier, output result can be thought of evidence, further more,the BPA of each classifier is determined, then the procession of the model identification must be improved.     

Technique of Information Fusion in Vehicle Control System

In the future, vehicle will be developed intelligently, which is based on the development of information technology. Now, information fusion technique is a new direction of information management. It has the advantages of more information, arrangements and methods and has been applied to a lot of research fields, The basic principle of information fusion is discussed and its applications in domestic and international modern vehicle control system are introduced. Moreover, the authors point out the key technology of information fusion and the bright prospects of its application in automotive field are proposed     

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.     

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.     

Fault diagnosis of rotating machinery based on SVM 2PTMC

A method is proposed to determine the priority class of binary tree SVM. Firstly, the vibration signals of rotating machinery are colleted through a rotating machinery fault experimental platform and data acquisition system. The signals are from 5 different conditions, i.e. rotor normal, rotor unbalance, rotor misalignment, rotor bearing inner ring cracks and rotor bearing outer ring cracks. Then the signals are disposed by zero-mean and the main frequency band of the vibration signals are reconstructed to, extract the dimensionless time domain as characteristic value. Finally, the priority class of SVM 2PTMC can be determined by the correct inspection rate of parallel SVM. Training samples can be completely divided in experiments, which verifies the effectiveness of this method.     

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.     

Fault diagnosis of rotating machinery based on fuzzy clusteringoptimized by chaos embedded particle swarm optimization

A method of weighted fuzzy clustering optimized by chaos embedded particle swarm algorithm(CPSO) is put forward and applied in vibration fault diagnosis of rotating machinery. In the method, CPSO is used to displace the traditional stochastic-gradient algorithm to optimize parameters of weighted fuzzy C-means (WFCM). The best clustering num and clustering centers are automatically attained according to clustering validity function. The experimental results show that the method effectively increases the convergence velocity and precision of WFCM and so does the correctness rate of fault diagnosis for rotating machinery.     

原生质体融合技术在枣育种中的应用展望

在枣树育种中,由于枣树自身的遗传特性,决定了枣树不能广泛运用常规杂交育种技术。针对这一困难,本文提出了应用枣树原生质体融合技术来实现枣树优良基因的重新组合,达到创造新种质,获得新品种的目的。通过对有关文献的分析,本文主要对原生质体及原生质体融合技术的研究进展和这一育种技术在枣育种中应用的先进性、实际应用中的可行性及应用过程中的关键步骤进行了综述。并对这一技术在枣树育种中的研究进展情况及应用中的问题和困难进行了探讨。     

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.