Smart Corrosion Monitoring in AA2055 Using Hidden Markov Models and Electrochemical Noise Signal Processing
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 r...
| Autores principales: | , , , , , , , , , |
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| Formato: | Artículo |
| Lenguaje: | inglés |
| Publicado: |
Molecular diversity preservation international
2025
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| Materias: | |
| Acceso en línea: | http://eprints.uanl.mx/30102/7/30102.pdf |
| _version_ | 1836640498147655680 |
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| author | Martínez Ramos, Cynthia Gaona Tiburcio, Citlalli Estupiñan López, Francisco Humberto Cabral Miramontes, José Ángel Maldonado Bandala, Erick Almeraya Calderón, Facundo Nieves Mendoza, Demetrio Baltazar Zamora, Miguel Ángel Landa Ruiz, Laura Galván Martínez, Ricardo Almeraya Calderón, Facundo |
| author_facet | Martínez Ramos, Cynthia Gaona Tiburcio, Citlalli Estupiñan López, Francisco Humberto Cabral Miramontes, José Ángel Maldonado Bandala, Erick Almeraya Calderón, Facundo Nieves Mendoza, Demetrio Baltazar Zamora, Miguel Ángel Landa Ruiz, Laura Galván Martínez, Ricardo Almeraya Calderón, Facundo |
| author_sort | Martínez Ramos, Cynthia |
| collection | Repositorio Institucional |
| description | 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. |
| format | Article |
| id | eprints-30102 |
| institution | UANL |
| language | English |
| publishDate | 2025 |
| publisher | Molecular diversity preservation international |
| record_format | eprints |
| spelling | eprints-301022025-07-03T14:17:54Z http://eprints.uanl.mx/30102/ Smart Corrosion Monitoring in AA2055 Using Hidden Markov Models and Electrochemical Noise Signal Processing Martínez Ramos, Cynthia Gaona Tiburcio, Citlalli Estupiñan López, Francisco Humberto Cabral Miramontes, José Ángel Maldonado Bandala, Erick Almeraya Calderón, Facundo Nieves Mendoza, Demetrio Baltazar Zamora, Miguel Ángel Landa Ruiz, Laura Galván Martínez, Ricardo Almeraya Calderón, Facundo TA Ingeniería General y Civil 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. Molecular diversity preservation international 2025-06-17 Article PeerReviewed text en cc_by_nc_nd http://eprints.uanl.mx/30102/7/30102.pdf http://eprints.uanl.mx/30102/7.haspreviewThumbnailVersion/30102.pdf Martínez Ramos, Cynthia y Gaona Tiburcio, Citlalli y Estupiñan López, Francisco Humberto y Cabral Miramontes, José Ángel y Maldonado Bandala, Erick y Almeraya Calderón, Facundo y Nieves Mendoza, Demetrio y Baltazar Zamora, Miguel Ángel y Landa Ruiz, Laura y Galván Martínez, Ricardo y Almeraya Calderón, Facundo (2025) Smart Corrosion Monitoring in AA2055 Using Hidden Markov Models and Electrochemical Noise Signal Processing. Materials, 18 (12). pp. 1-12. ISSN 1996-1944 https://www.mdpi.com/1996-1944/18/12/2865# 2865 |
| spellingShingle | TA Ingeniería General y Civil Martínez Ramos, Cynthia Gaona Tiburcio, Citlalli Estupiñan López, Francisco Humberto Cabral Miramontes, José Ángel Maldonado Bandala, Erick Almeraya Calderón, Facundo Nieves Mendoza, Demetrio Baltazar Zamora, Miguel Ángel Landa Ruiz, Laura Galván Martínez, Ricardo Almeraya Calderón, Facundo Smart Corrosion Monitoring in AA2055 Using Hidden Markov Models and Electrochemical Noise Signal Processing |
| thumbnail | https://rediab.uanl.mx/themes/sandal5/images/online.png |
| title | Smart Corrosion Monitoring in AA2055 Using Hidden Markov Models and Electrochemical Noise Signal Processing |
| title_full | Smart Corrosion Monitoring in AA2055 Using Hidden Markov Models and Electrochemical Noise Signal Processing |
| title_fullStr | Smart Corrosion Monitoring in AA2055 Using Hidden Markov Models and Electrochemical Noise Signal Processing |
| title_full_unstemmed | Smart Corrosion Monitoring in AA2055 Using Hidden Markov Models and Electrochemical Noise Signal Processing |
| title_short | Smart Corrosion Monitoring in AA2055 Using Hidden Markov Models and Electrochemical Noise Signal Processing |
| title_sort | smart corrosion monitoring in aa2055 using hidden markov models and electrochemical noise signal processing |
| topic | TA Ingeniería General y Civil |
| url | http://eprints.uanl.mx/30102/7/30102.pdf |
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