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Fault Diagnosis Based on Data Mining Methods

Author: JiangBiBo
Tutor: ZhaoHui
School: Tianjin University of Technology
Course: Control Theory and Control Engineering
Keywords: Troubleshooting Data Mining Fuzzy association rules Ant tree MATLAB GUI
CLC: TP311.13
Type: Master's thesis
Year: 2011
Downloads: 168
Quote: 1
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Abstract


With the rapid development of science and technology , modern production processes increasingly complex equipment systems under high load , high-power continuous operation under the conditions will inevitably fail , causing serious economic losses and casualties . Using equipment fault diagnosis system is to ensure efficient, safe and reliable operation of the important means to reduce the accident rate equipment and prolong life , increase the economic efficiency of enterprises of great significance. Traditional fault diagnosis method to solve the early fault diagnosis has played an important role, but due to technical limitations , there are still many shortcomings and deficiencies, the data mining techniques applied to fault diagnosis system, help to overcome these shortcomings and deficiencies . This paper discusses the fault diagnosis technology research situation , analyzes the limitations of the traditional fault diagnosis methods and shortcomings , and then the data mining technology for a detailed description of the commonly used algorithm of association rules and decision tree algorithms in-depth study By analyzing the advantages and disadvantages of the two algorithms , the two algorithms proposed improvement measures, and improved data mining algorithms applied to the fault diagnosis system . This article will compete agglomerative clustering algorithm is applied to fault diagnosis feature classification, elaborated association rules algorithm based on fuzzy fault diagnosis of the specific process . In addition, for the characteristics of decision tree algorithm , we propose a decision tree ant colony algorithm , the algorithm can effectively improve the classification accuracy , the simulation results prove the feasibility and effectiveness of the improved algorithm . Finally, the improved fuzzy association rules combined with the improved decision tree algorithm applied to fault diagnosis systems, applications SQLSERVER 2000 to establish a database , stored procedures historical data and preliminary processing of the data , using MATLAB GUI as a development tool , integrated application Database Toolbox establish a simplified fault diagnosis system , and through fault diagnosis examples demonstrate the correctness and feasibility .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer software > Program design,software engineering > Programming > Database theory and systems
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