Corrosion deterioration of materials is a major problem affecting economic, safety, and logistical issues, especially in the aeronautical sector. Detecting the correct corrosion type in metal alloys is very important to know how to mitigate the corrosion problem. Electrochemical noise (EN) is a corrosion technique used to characterize the behavior of different alloys and determine the type of corrosion in a system. The objective of this research is to characterize by EN technique different aeronautical alloys (Al, Ti, steels, and superalloys) using different analysis methods such as time domain (visual analysis, statistical), frequency domain (power spectral density (PSD)), and frequency–time domain (wavelet decomposition, Hilbert Huang analysis, and recurrence plots (RP)) related to the corrosion process. Optical microscopy (OM) is used to observe the surface of the tested samples. The alloys were exposed to 3.5 wt.% NaCl and H₂SO₄ solutions at room temperature. The results indicate that HHT and recurrence plots are the best options for determining the corrosion type compared with the other methods due to their ability to analyze dynamic and chaotic systems, such as corrosion. Corrosion processes such as passivation and localized corrosion can be differentiated when analyzed using HHT and RP methods when a passive system presents values of determinism between 0.5 and 0.8. Also, to differentiate the passive system from the localized system, it is necessary to see the recurrence plot due to the similarity of the determinism value. Noise impedance (Z_n) is one of the best options for determining the corrosion kinetics of one system, showing that Ti CP2 and Ti-6Al-4V presented 742,824 and 939,575 Ω·cm², while R_n presented 271,851 and 325,751 Ω·cm², being the highest when exposed to H₂SO₄.

Aluminum and its alloys find widespread applications across diverse industries such as the automotive, construction, and aeronautics industries. When these alloys come into contact with ambient air, an Al₂O₃ thin oxide layer is naturally formed, typically measuring 2 to 4 nm and exhibiting remarkable hardness and protective qualities, rendering the alloys corrosion-resistant in specific atmospheric and chemical environments. This study aimed to characterize the electrochemical behaviors of anodized AA2024 and AA7075 alloys within a complex three-component electrolyte composed of tartaric–phosphoric–sulfuric acid (TPSA) solutions. The anodized specimens were subsequently exposed to 3.5 wt.% NaCl solution at room temperature, and their electrochemical performances were meticulously evaluated using an electrochemical noise (EN) analysis in accordance with ASTM G-199, respectively. In the EN, three methods of data analysis were used: the time domain analysis (chaos analysis: application of Lyapunov exponent and dimension correlation), the frequency domain analysis (power spectral density, PSD), and the time–frequency domains analysis (Hilbert–Huang transform, HHT). Scanning electron microscopy (SEM) was used to observe the morphologies of the anodized surfaces. The results indicated that the AA2024-0, AA2024-1, and AA2024-2 alloys and the AA7075-2 and AA7075-3 samples exhibited mixed corrosion according to the Lyapunov constant, with a notable inclination towards localized corrosion when analyzed using the PSD and HHT methods. The surface was not homogenous, and the corrosion process was predominately localized in specific zones.

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.