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Study on the Technology of Oil Pollution Class Based on Image Processing

Author: JiangMingYan
Tutor: WangZuo
School: Harbin Institute of Technology
Course: Control Science and Engineering
Keywords: Oil pollution levels Edge Detection Learning vector quantization nerve network BP neural network
CLC: TP274
Type: Master's thesis
Year: 2011
Downloads: 22
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Abstract


The oil pollution quantitative reaction oil contaminated the extent is an important theoretical basis for oil pollution control . According to statistics, 70% -85 % of the hydraulic system failure caused by pollution due to oil . Therefore, the detection of fluid contamination levels , has a very important significance to improve the reliability of the hydraulic system , extending its life . Used testing equipment or expensive or inconvenient operation , or the detection accuracy is not high . In order to design a low cost , efficient, accurate and convenient particle analyzer independent oil pollution detection , this paper uses microscopic imaging and computer image processing technology , the overall system design . The system mainly consists of two parts of the image acquisition and image processing , focused on the use of computer image processing technology , identified pollution particles in the fluid image and the statistics of the relevant parameters , and then calculate the level of oil pollution . For this purpose , mainly to do the following contents : 1 , learning vector quantization (LVQ) neural network applications in particle edge detection . First, according to the fluid characteristics of the image , a recognition algorithm based on the average background of the particles , to obtain a target edge image . Second, starting from the edge points and the difference of the noise point extracted three both representative of the characteristic amount of the edge information has anti- noise ability feature vector consisting of a representative sample of the image information , and the target edge image composed training the neural network training samples . Finally, fluid image edge detection using the trained neural network . Simulation results show that the network can better edge detection of the particles . 2, BP neural network edge detection . Using the same training samples and learning vector quantization neural network to build a three-layer BP neural network . Simulation results show that the detection of the BP neural network is better than the LVQ neural network . 3, using the numeral operator , statistics, the number and size of the particles and the level of contamination of oil , which can be calculated . Meanwhile, in order to get a more accurate level studied a method to distinguish the glob bubbles and solid particles .

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CLC: > Industrial Technology > Automation technology,computer technology > Automation technology and equipment > Automation systems > Data processing, data processing system
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