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Research on Analyse and Recognition of Particle Image on Digital Image Processing

Author: LiuJianHui
Tutor: DuYongPing
School: Beijing Jiaotong University
Course: Mechanical Design and Theory
Keywords: Ferrography Image processing Feature Extraction Wear Debris Recognition
CLC: TH165.3
Type: Master's thesis
Year: 2007
Downloads: 273
Quote: 3
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Abstract


The mechanical equipment during operation , the relative movement of the friction pairs to produce large amounts of wear particles study proved the abrasive grains due to the different mechanisms of wear having respective characteristics . The microscopic particles ferrography to wear for the study, by the number and trend analysis of the various types of wear particles in the machine , monitoring the status of the machine which then predict machine failure may occur . With the development of computer technology , image processing , pattern recognition and artificial intelligence methods Ferrography combination of machinery and equipment research and development based on the automatic identification of the iron spectrum abrasive image intelligent condition monitoring and fault diagnosis system is to make the iron spectrum technology to adapt to the needs of modern machinery monitoring and an inevitable trend to play its industrial value . Summed image at home and abroad in recent years, the technology applied to iron spectrum analysis based on for the problems domestic Ferrography system , qualitative analysis around the abrasive image must solve image processing , feature extraction and abrasive recognition the three basic issues a series of work . First , image processing , novel mean shift filtering algorithms and competitive clustering algorithm unsupervised image automatic segmentation and the use of the method of mathematical morphology to complete the separation of the wear particles and image ; Second , in addition to the conventional feature extraction morphological features , this paper fractal theory introduction of abrasive boundary details and the measurement of the surface morphology , so that wear particle analysis is more diverse than in the past ; final abrasive recognition to improve the BP neural network and gray relational analysis theory , as well as their combination model based on the automatic identification of wear particles .

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CLC: > Industrial Technology > Machinery and Instrument Industry > Machinery Manufacturing Technology > Flexible manufacturing systems and flexible manufacturing cell > Fault diagnosis and maintenance
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