Martinez-Ramos Cynthia, Gaona-Tiburcio Citlalli, Estupiñan-López Francisco, Cabral-Miramontes Jose, Maldonado-Bandala Erick, Nieves-Mendoza Demetrio, Baltazar-Zamora Miguel Angel, Landa-Ruiz Laura, Galvan-Martinez Ricardo, Almeraya-Calderón Facundo
Universidad Autónoma de Nuevo León Centro de Investigación e Innovación en Ingeniería Aeronáutica (CIIIA), FIME, San Nicolás de los Garza 66455, Mexico.
Facultad de Ingeniería Civil, Universidad Veracruzana, Xalapa 91000, Mexico.
Materials (Basel). 2025 Jun 17;18(12):2865. doi: 10.3390/ma18122865.
This work explores the application of Hidden Markov Models (HMMs) for the classification and reconstruction of corrosion mechanisms in the aerospace-grade aluminum alloy AA2055 from the signals obtained by electrochemical noise (EN) analysis. Using the PELT algorithm to segment the signal based on relevant changepoints, distinct corrosion states within the segments are isolated and identified, including general, localized, and mixed corrosion based on statistical signal features, which are used to create the probabilistic structure of HMMs through the initiation, transition, and emission matrices. This study utilized a dataset composed of five electrolyte groups, each containing ten EN signals with 1024 data points per signal, totaling 51,200 data points. The model demonstrates that even with variability in signal quality, meaningful reconstruction is achievable, especially when datasets include distinct transient behavior.
这项工作探索了隐马尔可夫模型（HMMs）在根据电化学噪声（EN）分析获得的信号对航空级铝合金AA2055的腐蚀机制进行分类和重构方面的应用。使用PELT算法基于相关变化点对信号进行分段，基于统计信号特征隔离并识别各段内不同的腐蚀状态，包括全面腐蚀、局部腐蚀和混合腐蚀，这些特征用于通过起始、转移和发射矩阵创建HMMs的概率结构。本研究使用了一个由五个电解质组组成的数据集，每个组包含十个EN信号，每个信号有1024个数据点，总计51200个数据点。该模型表明，即使信号质量存在差异，也能够实现有意义的重构，特别是当数据集包含明显的瞬态行为时。
