Extraction of atmospheric corrosion monitoring sensor signals using MSSA and corrosion progress prediction with an LSTM model

Abstract

The sensitivity of corrosion signals to temperature fluctuations often presents significant challenges for signals acquired from corrosion sensors. The main objective of this study is to assess these signals collected by the atmospheric corrosion monitoring sensor based on strain measurement through the application of multivariate singular spectrum analysis (MSSA) for extracting corrosion signal data. The study results demonstrate that MSSA has consistently exhibited exceptional performance in isolating corrosion signals, particularly in scenarios where they are affected by inherent noise resulting from abrupt temperature changes, corrosion products and residual stress. Moreover, the corrosion signals extracted via MSSA were harnessed to create a long short-term memory (LSTM) model, with the aim of predicting future corrosion processes. The results of this study emphasize the effectiveness and reliability of the LSTM model in forecasting corrosion processes. This study holds a crucial role in the management of corrosion processes in materials exposed to atmospheric conditions, ensuring both structural integrity and industrial safety.