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The Research and Application of Fault Diagnosis Based on Pattern Recognition Technology

Author: ZhangYanJu
Tutor: LiGang
School: Hefei University of Technology
Course: Applied Computer Technology
Keywords: Fault Diagnosis Pattern Recognition Principal Component Analysis
CLC: TP18
Type: Master's thesis
Year: 2009
Downloads: 218
Quote: 1
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Abstract


The increasingly fierce market competition and increasingly demanding quality requirements, so the main attention has product quality, product yield and non-price competition has become the main means of international competition. The face of fierce competition in the quality of research and implementation of a comprehensive production process control and diagnosis is the primary means to improve product quality, enhance competitiveness. The fault diagnosis system is efficient advanced manufacturing systems, necessary for the protection of reliable operation, its on the one hand with the development of the manufacturing system passively follow the development, on the other hand, as diagnostic techniques and related hardware and software environment to enhance and improve its own initiative to develop . Analysis and discusses the basic concepts and theory of fault diagnosis, explore the existing domestic and international quality control method based on analysis of the shortcomings of the original process control techniques such as SPC, in the face of modern automated production processes proposed based on pattern recognition of fault diagnosis technology, and the algorithm is applied to the project \The main work and innovation of the papers are as follows: an overview of the model, the concept of pattern recognition and connotations; analysis pattern recognition system composed of the general model, classification and major tasks; explore several common methods of pattern recognition and analysis The advantages and disadvantages of the two methods. For a large number of original sample data is difficult to accurately diagnose, explore two common methods of data Statute: feature selection and feature extraction. Principal component analysis (PCA) to reduce dimension of sample data collected online, select the main characteristics constitute signs vector. In response to the sample uncertainty exists in the production process and disorderly noise case, the establishment of the fault diagnosis model, the design of fault diagnosis based on pattern matching techniques, root cause failure identification error recognition rate alarm rate . Using MATLAB program to implement the algorithm, test and analysis of algorithms, and has achieved the desired results. More comprehensive use of the theory, methods and techniques, combined with information integration planning for enterprise automation project of \satisfactory results.

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CLC: > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory
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