Using fuzzy sets to estimate the geometric parameters of surface damage

被引：3

作者：

Konovalenko I.V. ^{[1
]}

Pastukh O.A. ^{[1
]}

Marushchak P.O. ^{[1
]}

机构：

[1] Ivan Pulyui Ternopil National Technical University, ul. Russkaya 56, Ternopil

来源：

Optoelectronics, Instrumentation and Data Processing | 2016年 / 52卷 / 04期

关键词：

damage detection; fuzzy distance; fuzzy geometry; fuzzy quasi-point; image recognition;

D O I：

10.3103/S8756699016040014

中图分类号：

学科分类号：

摘要：

This paper considers the influence of the variable parameters of a surface defect detection algorithm on the result of its operation. A method for estimating the influence of the variable parameters on the recognized geometric characteristics of the surface defect network is proposed. This method is based on representing the basic zones of the skeleton of the surface damage network in the form of compact fuzzy sets in two-dimensional space (fuzzy quasi-points). The set of points of the recognized object obtained for various combinations of the test algorithm parameters is represented in the form of a fuzzy set with a certain membership function. A method for calculating the geometric parameters of the damage network (length and slope) by means of fuzzy geometry is considered, and its use for determining the geometric parameters of the damage network of a continuous casting roller is demonstrated. © 2016, Allerton Press, Inc.

引用

页码：319 / 327

页数：8

共 12 条

[1]

Gavilan M., Balcones D., Marcos O., Et al., Adaptive road Crack Detection System by Pavement Classification, Sensors, 11, 10, pp. 9628-9657, (2011)

[2]

Konovalenko I., Maruschak P., Menou A., Et al., A Novel Algorithm for Damage Analysis of Fatigue Sensor by Surface Deformation Relief Parameters, Intern. Symp. on Operational Research and Applications, pp. 678-684, (2013)

[3]

Maruschak P.O., Panin S.V., Ignatovich S.R., Et al., Influence of Deformation Process in Material at Multiple Cracking and Fragmentation of Nanocoating, Theor. Appl. Fracture Mech., 57, 1, pp. 43-48, (2012)

[4]

Bornert M., Bremand F., Doumalin P., Et al., Assessment of Digital Image Correlation Measurement Errors: Methodology and Results, Exp. Mech., 49, 3, pp. 353-370, (2009)

[5]

Konovalenkoand I.V., Marushchak P.O., Error Analysis of an Algorithm for Identifying Thermal Fatigue Cracks, Avtometriya, 47, 4, pp. 49-57, (2011)

[6]

Bishop C.M., Pattern Recognition and Machine Learning, (2006)

[7]

Maruschak P., Gliha V., Konovalenko I., Et al., Physical Regularities in the Cracking of Nanocoatings and a Method for an Automated Determination of the Crack-Network Parameters, Materials and Technology, 46, 5, pp. 525-529, (2012)

[8]

Panin S.V., Titkov V.V., Lyubutin P.S., Efficiency of Vector Field Filtration Algorithms in Estimating Material Strain by the Method of Digital Image Correlation, Avtometriya, 49, 2, pp. 57-67, (2013)

[9]

Konovalenko I.V., Maruschak P.O., Automated Method for Studying the Deformation Behavior of a Material Damaged by a Thermal Fatigue Crack Network, Avtometriya, 49, 3, pp. 36-43, (2013)

[10]

Mordeson J.N., Nair P.S., Fuzzy Mathematics: An Introduction for Engineers and Scientists, (2001)

← 1 2 →