Pitting Judgment Model Based on Machine Learning and Feature Optimization Methods

Abstract

Pitting corrosion seriously harms the service life of oil field gathering and transportation pipelines, which is an important subject of corrosion prevention. In this study, we collected the corrosion data of pipeline steel immersion experiment and established a pitting judgment model based on machine learning algorithm. Feature reduction methods, including feature importance calculation and pearson correlation analysis, were first adopted to find the important factors affecting pitting. Then, the best input feature set for pitting judgment was constructed by combining feature combination and feature creation. Through receiver operating characteristic (ROC) curve and area under curve (AUC) calculation, random forest algorithm was selected as the modeling algorithm. As a result, the pitting judgment model based on machine learning and high dimensional feature parameters (i.e., material factors, solution factors, environment factors) showed good prediction accuracy. This study provided an effective means for processing high-dimensional and complex corrosion data, and proved the feasibility of machine learning in solving material corrosion problems.

Probing the randomness of the local current distributions of 316 L stainless steel corrosion in NaCl solution

“…As pitting events are relatively rare and unpredictable, pit initiation has often been considered stochastic in the literature [3][4][5][6]. From a macroscopic perspective, describing the pitting process as events occurring randomly in time and space has seemed straightforward [3,[5][6][7].…”

“…As pitting corrosion is sensitive to slight specimen-to-specimen variability [5,11], deterministic modelling (linking specific outputs with specific inputs) is hardly transposable to slightly different scenarios. The conclusion from extensive data analysis is that a reliable prediction of pitting corrosion could only be made in probabilistic terms [12,13]. Stochastic approaches have been elaborated to handle such high randomness, and the large scatter typically observed in pitting potential/induction time [5,14,15].…”

“…Stochastic approaches have been elaborated to handle such high randomness, and the large scatter typically observed in pitting potential/induction time [5,14,15]. Statistical methods used to simulate local corrosion are often based on the measurement of corrosion depths (including the theory of extreme value analysis, the Frechet extreme value distribution, and Bayesian inference [7,16]).…”

Machine Learning-Assisted Prediction of Corrosion Behavior of 7XXX Aluminum Alloys

“…Prior materials knowledge is also utilized to perform mathematical operations on parameters such as the electronic, atomic, and crystallographic features of elements, as well as thermodynamic and kinetic parameters of materials. These parameters are transformed into factors to train machine learning models, enhancing the generalization ability of the latter [27,28].…”

Recognition of Corrosion State of Water Pipe Inner Wall Based on SMA-SVM under RF Feature Selection

“…By combining the image texture feature extraction algorithm and the support vector machine classifier with the differential pollination optimization, the detection accuracy of the inner wall of the water supply pipe was 92.81%. Qu, ZH [14] et al proposes a method to detect pitting corrosion by combining feature extraction and random forest algorithms, without studying more corrosion types. Nhat-Duc [15] proposed a LSHADE meta-heuristic algorithm to optimize the SVM model to detect pitting on the surface of components, with an accuracy of 91.80%, the accuracy rate needs to be improved.…”