Research on Aircraft LY12CZ Aluminum Alloy Corrosion Damage Prediction Based on ARIMA Model

Liu, Zhi Guo; Mu, Zhi Tao

doi:10.4028/www.scientific.net/amr.308-310.1016

Cited by 4 publications

(2 citation statements)

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“…The results of typical corrosion prediction cases based upon physical and statistical methods are shown in Table 1. Statistical methods are applied to build prediction models that are based on actual corrosion rate data, such as linear regression (LR) [9], autoregressive moving average (ARMA) [10], autoregressive integrated moving average (ARIMA) [11], and grey model (GM) [12]. In actual production conditions, statistical methods are mostly used for corrosion rate prediction.…”

Section: Introductionmentioning

confidence: 99%

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Research and Application for Corrosion Rate Prediction of Natural Gas Pipelines Based on a Novel Hybrid Machine Learning Approach

Yu²,

Zhu³

et al. 2023

Coatings

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An accurate and stable prediction of the corrosion rate of natural gas pipelines has a major impact on pipeline material selection, inhibitor filling process, and maintenance schedules. At present, corrosion data are impacted by non-linearity and noise interference. The traditional corrosion rate prediction methods often ignore noise data, and only a small number of researchers have carried out in-depth research on non-linear data processing. Therefore, an innovative hybrid prediction model has been proposed with four processes: data preprocessing, optimization, prediction, and evaluation. In the proposed model, a decomposing algorithm is applied to eliminate redundant noise and to extract the primary characteristics of the corrosion data. Stratified sampling is applied to separate the training set and the test set to avoid deviation due to the sampling randomness of small samples. An improved particle swarm optimization algorithm is applied to optimize the parameters of support vector regression. A comprehensive evaluation of this framework is also conducted. For natural gas pipelines in southwest China, the coefficient of determination and mean absolute percentage error of the proposed hybrid model are 0.925 and 5.73%, respectively, with better prediction performance compared to state-of-the-art models. The results demonstrate the best approach for improving the prediction accuracy of the proposed hybrid model. This can be applied to improve the corrosion control effect and to support the digital transformation of the corrosion industry.

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Section: Introductionmentioning

confidence: 99%

“…The results demonstrate that SVMs have an excellent generalization ability, and their prediction performance is better than that of NNs, even with small samples [1, 21,22]. Liu Z G et al [11] ARIMA model The model can effectively predict the size and development trend of corrosion depth.…”

Section: Introductionmentioning

confidence: 99%