Dissertation > Excellent graduate degree dissertation topics show

Study and Realization of Wear Debris Image Computer Analysis and Recognition Methods

Author: LiDaGuang
Tutor: ZuoDaShun
School: Wuhan University of Technology
Course: Signal and Information Processing
Keywords: Ferrography Abrasive Image processing Pattern Recognition Neural Networks
CLC: TP391.41
Type: Master's thesis
Year: 2005
Downloads: 159
Quote: 7
Read: Download Dissertation

Abstract


Iron spectrum technology is abrasion wear particle analysis based fault diagnosis method. Ferrography wear particle analysis because of its high efficiency, economy and in machinery and equipment monitoring, troubleshooting and preventive maintenance has been widely used. The abrasive identify core is the iron spectrum abrasive analysis. Due to the diversity and complexity of the abrasive, abrasive identification no mature theory to guide the abrasive identify mainly done manually by experts. Computer image processing and artificial intelligence the Debris Analysis intelligent powerful. In this paper, the basic theory of the iron spectrum of fault diagnosis based on combination of image processing principles and methods, research based the image processing Ferrography coverage area to cover the area of ??the return of a group of oil-like iron spectrum analysis to verify the method in ferrography fault diagnosis and prediction; combined the principles and methods of pattern recognition research applications of neural networks in the abrasive type identification, and on this basis to achieve a the abrasive of real-time analysis. This study is important for the promotion of the wear particle analysis in mechanical wear monitoring. The main content of this paper are: 1. Comprehensive relevant literature at home and abroad, the development and current status of Ferrographic Wear analysis techniques were reviewed, combined with the requirements of the research on the main contents of this paper. Analyzed the Mechanism and Classification of wear, abrasive wear corresponding classification and characteristics. Of basic abrasive type, characteristics, ingredients and produce mechanism and equipment wear state. Abrasive image preprocessing for image smoothing, filtering, edge detection based on digital image processing technology, analysis, discussion. Focuses on the the different circumstances abrasive image segmentation technology. 4 on the basis of a large number of experiments, regression analysis of the iron spectral data for a group of oil samples by feasibility based the image processing Ferrography quantitative analysis. 5. Analysis to calculate the the types characteristic parameters of the abrasive grain morphology, texture, and color, and to establish a more complete description of the abrasive characteristics. 6 to discuss the application of neural networks in the analysis of the abrasive category. On the basis of video capture, image processing and pattern recognition, knowledge combine and abrasive image obtained in real time on the platform of the VC. Net, segmentation, feature parameter extraction and type recognition.

Related Dissertations

  1. High Speed Frequency Measurment and Non-Linearity Correction of Frequency Modulated Capacitive Displacement Sensor,TH822
  2. Research on Temperature Measurement Technique Based on CCD Image Sensor,TH811
  3. The Classification of High Dimsnsion Flew Field Based on Manifold Learning,V231.3
  4. Research on Basic Algorithms of Digital Image Processing and Implementation with FPGA,TP391.41
  5. Research of Images Enhancing Algorithms on Fog or Backlighting Conditions and Implementation with Hardwares,TP391.41
  6. Research on Text Classification Based on Biomimetic Pattern Recongnition,TP391.1
  7. Research on the Image Real-Time Acquisition, Storage and Image Processing System,TP391.41
  8. The Research of Moving Object Tracking System Based on Embeded Image Process Unit,TP391.41
  9. Research on Visual Servo System of Mechanical ARM,TP242.6
  10. Application Research of Digital Image Processing on Container Inspection,TP274.4
  11. Three-Dimensional Numerical Modelling for the Head and the Electric Field Analysis of DBS,R742.5
  12. Application of Semi-Structure in Leisure Dress,TS941.2
  13. Research on Anti-periodic Solutions of Delayed Cellular Neural Networks without Assuming Global Lipschitz Conditions,TP183
  14. Designs and Applications of Fuzzy Synthetic Evaluation Models Based on Parallel Algorithms,TP18
  15. Research on Nondestructive Detection Technology for External Qualities of Papayas Based-on Vision,S667.9
  16. Research on Detecting Optical Fiber Geometric Parameter Based on Machine Vision,TN253
  17. Research on Identification System of Cashmere and Wool Fiber,TS101.921
  18. Intrusion detection based on the ultrasonic echo envelope in the military security patrols,E919
  19. Design of Garlic Planting Sorting Machine Based on Image Processing,S223.2
  20. Study on Detection and Grading of ’Jiro’ Persimmon’s External Quality Based on Computer Vision,S665.2
  21. Flow Behavior Description of Two-Phase Flow Based on Image Processing Techniques,TP391.41

CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
© 2012 www.DissertationTopic.Net  Mobile