Defining а Time-Average Growth Rate of a Corrosion Defect from the Data of Any Number of Inspections

Авторы: Galakhar A.S. Опубликовано: 16.02.2022
Опубликовано в выпуске: #1(100)/2022  
DOI: 10.18698/1812-3368-2022-1-22-38

Раздел: Математика и механика | Рубрика: Вычислительная математика  
Ключевые слова: corrosion modelling, defect initiation, integrity prediction, machine learning, residual operating life, sizing uncertainty, thin-walled structure


Although the corrosion rate varies with time depending on changing ambient conditions the time-average corrosion rate becomes stable 5 years later defect initiation. Therefore, a remaining useful life of a thin-walled structure is generally estimated using time-average growth rates of its revealed corrosion defects determined after periodic inspections according to data of inaccurate measurements of a remaining wall thickness. This paper presents a new approach to defining both initiation time of a corrosion defect and the time-average growth rate of the defect from the data of any number of inspections. A ratio of measured remaining to initial wall thickness is taken complying with a beta-distribution at a point of measurement, as it varies in finite interval [0; 1]. The parameters of the beta-distribution are obtained from analysis of measurement data and sizing uncertainties. Initiation time of the corrosion defect is determined with the method of maximum likelihood. The estimates of both mathematical expectation and variance of time-average growth rate of the corrosion defect are obtained using k-nearest neighbours (kNN) method from the data of all inspections. The presented approach is validated in a virtual experiment where both the true time of initiation, and the true time-average corrosion rate are specified

Please cite this article as:

Galakhar A.S. Defining a time-average growth rate of a corrosion defect from the data of any number of inspections. Herald of the Bauman Moscow State Technical University, Series Natural Sciences, 2022, no. 1 (100), pp. 22--38. DOI: https://doi.org/10.18698/1812-3368-2022-1-22-38


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