Application of Improved Genetic Algorithm in Network Fault Diagnosis Expert System

Knowledge acquisition is the “bottleneck” of building an expert system. Based on the optimization model, an improved genetic algorithm applied to knowledge acquisition of a network fault diagnostic expert system is proposed. The algorithm applies operators such as selection, crossover and mutation to evolve an initial population of diagnostic rules. Especially, a self adaptive method is put forward to regulate the crossover rate and mutation rate. In the end, a knowledge acquisition problem of a simple network fault diagnostic system is simulated, the results of simulation show that the improved approach can solve the problem of convergence better.

Design and Implementation of Urban-rural Power Grid Dispatching Fault Diagnosis Expert System

The paper designs the urban-rural power grid dispatching fault diagnosis expert system which acquires fault information by SCADA system of automatic system of urban-rural power grid, and uses artificial intellegence method to analyze fault information and make fault diagnosis. The paper implements the core part of the fault expert system the design of knowledge base and fault inference engine.

A Simulation Study on Boiler Fault Diagnosis Expert System

The boiler is one of the key parts of power station. Monitoring the operation of the boiler automatically, continuously and accurately is very important. In case of malfunctions, an expert system might be helpful to find the sources of faults, and thereafter will assist the operator to act correctly and to prevent the fault from further development.In this paper, a simulation study of an expert system, which can diagnose the running faults of boiler system, is conducted. Shallow and deep two-layer knowledge base structure is used in this two layers of knowledge base. So. this kind of expert system can overcome the disadvantages of the old types of expert systems, which are only able to deal with experience rules base.A simulator of power boiler is used for the research on fault diagnose expert system. This boiler simulator gives all necessary information for the diagnosis. The knowledge base of expert system is built in hierarchies, so the speed of reasoning may be invreased. After the reasoning the

A Fault Diagnosis Expert System for a Heavy Motor Used in a Rolling Mill

A fault diagnosis expert system for a heavy motor used in a rolling mill is established in this paper. The fault diagnosis knowledge base was built, and its knowledge was represented by production rules. The knowledge base includes daily inspection system, brief diagnosis system and precise diagnosis system. A pull down menu was adopted for the management of the knowledge base. The system can run under the help of expert system development tools. Practical examples show that the expert system can diagnose faults rapidly and precisely.

STUDY OF A FAULT DIAGNOSIS EXPERT SYSTEM FOR SYNTHETIC MINING SYSTEM HYDRAULIC SUPPORT

Fault diagnosis expert system for hydraulic support is studied.The system is achieved by Turbo prolong Language, it summaries the experience of the domain expert and sets up a fault tree, knowledge base is developed by a productive rule.According to the feature of diagnosis, the system selects forward non determination inferring and limited depth first search strategy.It can accomplish expert diagnosis of more than 50 kinds faults in hydraulic support.

Research on the Algorithm of Avionic Device Fault Diagnosis Based on Fuzzy Expert System

Based on the fuzzy expert system fault diagnosis theory, the knowledge base architecture and inference engine algorithm are put forward for avionic device fault diagnosis. The knowledge base is constructed by fault query network, of which the basic ele- ment is the test-diagnosis fault unit. Every underlying fault cause＇s membership degree is calculated using fuzzy product inference algorithm, and the fault answer best selection algorithm is developed, to which the deep knowledge is applied. Using some examples the proposed algorithm is analyzed for its capability of synthesis diagnosis and its improvement compared to greater membership degree first principle.

Study on Missile Intelligent Fault Diagnosis System Based on Fuzzy NN Expert System

In order to study intelligent fault diagnosis methods based on fuzzy neural network (NN) expert system and build up intelligent fault diagnosis for a type of missile weapon system, the concrete implementation of a fuzzy NN fault diagnosis expert system is given in this paper. Based on thorough research of knowledge presentation, the intelligent fault diagnosis system is implemented with artificial intelligence for a large-scale missile weapon equipment. The method is an effective way to perform fuzzy fault diagnosis. Moreover, it provides a new way of the fault diagnosis for large-scale missile weapon equipment.

Design and implementation of an expert system for remote fault diagnosis in ship lift

In this paper an expert system for remote fault diagnosis in the ship lift was developed by analysis of the fault tree and combination with VPN. The fault tree was constructed based on the operation condition of the ship lift. The diagnosis model was constructed by hierarchical classification of the fault tree structure, and the inference mechanism was given. Logical structure of the fault diagnosis in the ship lift was proposed. The implementation of the expert system for remote fault diagnosis in the ship lift was discussed, and the expert system developed was realized on the VPN virtual network. The system was applied to the Gaobaozhou ship lift project, and it ran successfully.

Machinery fault diagnosis expert system based on case-based reasoning

A mechinery fault diagnosis expert system based on case-based reasoning (CBR) technology was established. The process of the CBR fault diagnosis is analyzed from three main aspects: expression and memory, retrieving and matching, and modification and maintenance of a case. The results indicate that the CBR method is flexible and simple to implement, and it has strong self-studying ability. Using a large enough number of case reasoning sets, it can accumulate the experience of problem solving, avoid the difficulty of knowledge acquisition, shorten the course of solving problems, improve efficiency of reasoning, and save the time of developing.

A Study on FMS's Real-time Fault Diagnosis Expert System

ＡＳｔｕｄｙｏｎＦＭＳ＇ｓＲｅａｌ－ｔｉｍｅＦａｕｌｔＤｉａｇｎｏｓｉｓＥｘｐｅｒｔＳｙｓｔｅｍＸＩＡＯＸｉａｎｇｓｈｅｎｇ；ＬＩＵＷｅｎｊｉａｎ；ＳＵＢａｏｈｕａ；ＭＡＹｕｌｉｎ（肖祥胜，刘文剑，苏宝华，马玉林）（ＦＭＳＲｅｓｅａｒｃｈＣｅｎｔｅｒ...

Learning Fault Diagnosis Knowledge for a FCC Expert System by Genetic Algorithms

This paper introduces a GA-based Fault Matrix Learning System(GAFMLS) which applies Genetic Algorithms to a FCC expert system (FCCES) to learn a near-optimal fault matrix used in the fault diagnosis of an oil catalytic and cracking unit. The practical running results show that more effective fault matrixes can be generated by GAFMLS, and the reliability and precision of FCC expert system are improved.

Expert Experience and Data-Driven Based Hybrid Fault Diagnosis for High-SpeedWire Rod Finishing Mills

The reliable operation of high-speed wire rod finishing mills is crucial in the steel production enterprise.As complex system-level equipment,it is difficult for high-speed wire rod finishing mills to realize fault location and real-time monitoring.To solve the above problems,an expert experience and data-driven-based hybrid fault diagnosis method for high-speed wire rod finishing mills is proposed in this paper.First,based on its mechanical structure,time and frequency domain analysis are improved in fault feature extraction.The approach of combining virtual value,peak value with kurtosis value index,is adopted in time domain analysis.Speed adjustment and side frequency analysis are proposed in frequency domain analysis to obtain accurate component characteristic frequency and its corresponding sideband.Then,according to time and frequency domain characteristics,fault location based on expert experience is proposed to get an accurate fault result.Finally,the proposed method is implemented in the equipment intelligent diagnosis system.By taking an equipment fault on site,for example,the effectiveness of the proposed method is illustrated in the system.

STUDY OF REAL-TIME EXPERT SYSTEM TOOL FOR INDUSTRIAL FAULT MONITORING AND DIAGNOSIS

From the requirements of industrial production,an integrated fault monitoring,diagnosis and repairing system is suggested in this paper. This new scheme of fault monitoring and diagnosis system is realized by a master-slave real-time expert system,and a real-time expert system tool for this system is also developed accordingly. As an example of application of this tool ,a realtime expert system for fault monitoring and diagnosis on DC mine hoist is developed. Experiments show that this tool possesses better supporting environment, strong knowledge acquisition ability, and convenience for use. The system developed by this tool not only meets the real-time requirement of DC hoist,but also can give correct diagnosis results.

Fault diagnosis method of link control system for gravitational wave detection

To maintain the stability of the inter-satellite link for gravitational wave detection,an intelligent learning monitoring and fast warning method of the inter-satellite link control system failure is proposed.Different from the traditional fault diagnosis optimization algorithms,the fault intelligent learning method pro-posed in this paper is able to quickly identify the faults of inter-satellite link control system despite the existence of strong cou-pling nonlinearity.By constructing a two-layer learning network,the method enables efficient joint diagnosis of fault areas and fault parameters.The simulation results show that the average identification time of the system fault area and fault parameters is 0.27 s,and the fault diagnosis efficiency is improved by 99.8%compared with the traditional algorithm.

Development of Fault Diagnosis System for Spacecraft Based on Fault Tree and G2

Some ideas in the development of fault diagnosis system for spacecraft are introduced. Firstly, the architecture of spacecraft fault diagnosis is proposed hierarchically with four diagnosis frames, i.e., system level, subsystem level, component level and element level. Secondly, a hierarchical diagnosis model is expressed with four layers, i.e., sensors layer, function layer, behavior layer and structure layer. These layers are used to work together to accomplish the fault alarm, diagnosis and localization. Thirdly, a fault-tree-oriented hybrid knowledge representation based on frame and generalized rule and its relevant reasoning strategy is put forward. Finally, a diagnosis case for spacecraft power system is exemplified combining the above with a powerful expert system development tool G2.

Application of Maximum Probability Approach to the Fault Diagnosis of a Servo System

In an actual control system, it is often difficult to find out where the faults are if only based on the outside fault phenomena, acquired frequently from a fault system. So the fault diagnosis by outside fault phenomena is considered. Based on the theory of fuzzy recognition and fault diagnosis, this method only depends on experience and statistical data to set up fuzzy query relationship between the outside phenomena (fault characters) and the fault sources (fault patterns). From this relationship the most probable fault sources can be obtained, to attain the goal of quick diagnosis. Based on the above approach, the standard fuzzy relationship matrix is stored in the computer as a system database. And experiment data are given to show the fault diagnosis results. The important parameters can be on line sampled and analyzed, and when faults occur, faults can be found, the alarm is given and the controller output is regulated.

Robust fault diagnosis with disturbance rejection and attenuation for systems with multiple disturbances

The fault diagnosis problem is investigated for a class of nonlinear neutral systems with multiple disturbances.Time-varying faults are considered and multiple disturbances are supposed to include the unknown disturbance modeled by an exo-system and norm bounded uncertain disturbance.A nonlinear disturbance observer is designed to estimate the modeled disturbance.Then,the fault diagnosis observer is constructed by integrating disturbance observer with disturbance attenuation and rejection performances.The augmented Lyapunov functional approach,which involves the tuning parameter and slack variable,is applied to make the solution of inequality more flexible.Finally,applications for a two-link robotic manipulator system are given to show the efficiency of the proposed approach.

Expert fault diagnosis system for leaching process in zinc hydrometallurgy

Leaching process is the first step in zinc hydrometallurgy, which involves the complex chemical reactions for dissolving zinc bearing material in dilute sulfuric acid. Ensuring the safe running of the process is a key point in the operation. An expert fault diagnosis system for the leaching process was proposed, which has been implemented in a nonferrous metals smeltery. The system architecture and the diagnosis procedure were presented, and the rule models with the certainty factor were constructed based on the empirical knowledge, empirical data and statistical results on past fault countermeasures, and an expert reasoning strategy was proposed which employs the rule models and Beyes presentation and combines forward chaining and backward chaining. [

Fault Diagnosis of Vehicle Transmission System Based on Rough Set Theory

Rough set theory is used to treat the data of vehicle transmission system faults. The minimum fault feature vector can be obtained by calculating the importance and dependency of each attribute. Real time diagnosis, as a result, can be actualized. Ultimate decision making can be done by analyzing the consistency of decision information. The result shows that rough set theory is useful and possesses its unique merits in this field.

RESEARCH ON EXPERT SYSTEM OF FAULT DETECTION AND DIAGNOSING FOR PNEUMATIC SYSTEM OF AUTOMATIC PRODUCTION LINE

Fault detection and diagnosis for pneumatic system of automatic productionline are studied. An expert system using fuzzy-neural network and pneumatic circuit fault diagnosisinstrument are deigned. The mathematical model of various pneumatic faults and experimental deviceare built. In the end, some experiments are done, which shows that the expert system usingfuzzy-neural network can diagnose fast and truly fault of pneumatic circuit.