پديد آورنده :
صالحي، غلامحسين
عنوان :
تشخيص عيوب خارج از مركزي غلتك هاي سيستم كشش بااستفاده از آناليز صدا و شبكه هاي عصبي
مقطع تحصيلي :
كارشناسي ارشد
گرايش تحصيلي :
مهندسي نساجي
محل تحصيل :
اصفهان: دانشگاه صنعتي اصفهان، دانشكده نساجي
صفحه شمار :
ده، 85ص.: مصور
يادداشت :
ص.ع. به فارسي و انگليسي
استاد راهنما :
محمد شيخ زاده، داريوش سمناني
توصيفگر ها :
عيوب پريوديك نخ , آناليز فوريه , پردازش سيگنال
تاريخ نمايه سازي :
4/10/90
استاد داور :
محمد قانع، حسين حسني
تاريخ ورود اطلاعات :
1396/10/12
چكيده فارسي :
به فارسي و انگليسي: قابل رويت در نسخه ديجيتالي
چكيده انگليسي :
Detectecting the Roller Eccentricity of Draft System Using Sound Analysis and Neural network Gholamhosein salehi farsani g salehi@txt iut ac ir Date of Submission 2011 07 13 Department of Textile Engineering Isfahan University of Technology Isfahan 84156 83111 Iran Degree M Sc Language Farsi Supervisor M sheikhzadeh m sh110@cc iut ac ir D semnani d semnani@cc iut ac irAbstract Most of industrial factories as well as textile factories interest to increase quality of production profit andpromote along decreasing their expenses One of the most important effective factors to achieve above goals isfavorable function of the producing plants distinguish the faults and remove them rapidly Because havingidentified the faults specially the mechanical ones it would be possible to correct the deficits find the shaftsroller eccentricity and rollers creating periodic faults and decreasing the quality of the threads highly Thestudy of the problems connected with textile machines and their rollers has formed the basis of previousresearch work These problems have attracted the attention of researchers for decades This investigation hasbeen revealed so much of principle and theory of problems assessment from voice analyzing The problems arefound in quality control unite by Ouster plant so a defined length of the thread passes through the plant andthecurve plant draws the mass changes by which they find the faults that is by virtue of the tests conducted onthe products and computations they find the origin of the fault in the plant but this is not a favorable methodbecause the faults are not often found because of test and computation errors or perhaps it lasts a long time thecontrol section finds the fault so many deficit products are produced because of high speed of the plants led tohigh expense and low output So if it is possible to find the find online the output increases considerably Inthis study we try to execute the primary step of finding the faults rapidly through the sounds of the rollers Fivetypes of rollers were selected in order to distinguish the faults of the rollers out of the center through the sound They should not select the eccentricity rollers primarily because we are to distinguish the faults out of thecenter so first they should become sure that the rollers work well by indicator watch Then the rollers arecoated lathed and four of them of 0 5 1 1 25 and 1 5 mm are led out of center the rollers are put on themachine their sounds are saved on the disc the saved data Time and frequencies are given to LVQ network Some data not present in the network previously are given to it to distinguish the fault s The results showedthat the time data were not effective to separate the faults the frequencies were very sufficient in a manner thatthe categorization by the network was right in 99 percent of thecases Key words 1 Periodic faults of yarn 2 Neural network 3 Fourier analysis 4 Signal processing
استاد راهنما :
محمد شيخ زاده، داريوش سمناني
استاد داور :
محمد قانع، حسين حسني
