Surface characterization and corrosion behavior of 90/10 copper-nickel alloy in seawater from Xiamen bay at 30 °C for 56 days were investigated in this study. The results indicated that the corrosion product layer was mainly a mixture of CuO, Cu₂O, and Cu(OH)₂, with a transition to CuCl, CuCl₂, and Cu₂(OH)₃Cl during the corrosion process. However, as corrosion proceeds, the resistance of the product film was reduced due to its heterogeneous and fairly porous structures, which led to local corrosion of the alloy. The corrosion potentials (E_corr) increase while corrosion current densities (I_corr) decrease with time because of the formation of protective oxide film.

Piezoelectric (PZT) ceramic elements are often subjected to complex loads during in- service lifetime in structural health monitoring (SHM) systems, and debonding of both excitation actuators and receiving sensors have a negative effect on the monitoring signals. A first systematic investigation of debonding behaviors by considering actuators and sensors simultaneously was performed in this paper. The debonding areas of actuators were set in different percentage range from 0% to 70%, and sensors in 0%, 20%, 40% and 60%. The signal-based monitoring method was used to extract the characteristic parameters of both the amplitudes and phases of received signals. Experimental results revealed that as the debonding areas of the actuators increase, the normalized amplitude appears a quick decrease before 35% debonding area of actuators and then a slow rise until 60% of debonding reached. This may be explained that the 35% debonding turning point correspond to the coincidence of the excitation frequencies of peripheral actuators with the inherent frequency of the central piezoelectric sensor, and the 60% be the result of the maximum ability of piezoelectric sensor. The degrees of debonding of actuators and sensors also have significant influence on the phase angle offset, with large debonding of actuators increases the phase offset sharply. The research work may provide useful information for practical monitoring of SHM systems.

In this work, the corrosion behavior and mechanical properties of 30CrMnSiA high-strength steel under a harsh marine atmosphere environment were systematically studied using accelerated test technology, along with corrosion kinetic analysis, microstructure and phase composition analysis, electrochemical measurements, and mechanical property tests. The influence of corrosion time on corrosion kinetics was characterized by the weight loss method. The corrosion layer and its product evolution were analyzed by SEM, EDS, XRD, and XPS. The corrosion behavior of steel was studied by a potentiodynamic polarization curve and EIS. Finally, the influence of corrosion on mechanical properties was studied by tensile and fatigue tests. The results show that 30CrMnSiA high strength steel has good corrosion resistance in a harsh marine atmosphere environment. Its corrosion behavior is cyclical: the outer rust layer exfoliated, the inner rust layer became the outer rust layer, and the matrix became inner rust due to the attack by the corrosive medium. The rust layer had a great protective effect on the matrix. The mechanical properties of 30CrMnSiA high-strength steel were reduced under the corrosive environment, and corrosion had a significant effect on its fatigue resistance.

In this work, the effects of the tropical marine atmospheric environment on the corrosion behaviour of the 7B04-T74 aluminium alloy were systematically investigated by using accelerated testing, together with corrosion kinetic analysis, microstructure observation, product composition analysis, and potentiodynamic polarization curve tests. The weight loss method was used for the corrosion kinetics analysis. The surface morphology and corrosion products transformation law were investigated by OM, SEM, EDS, and XPS. The electrochemical characteristics were studied using potentiodynamic polarization curves. The research indicated that the 7B04-T74 aluminium alloy has eminent corrosion resistance in the tropical marine atmospheric environment. Localized pitting corrosion occurred rapidly in the tropical marine atmosphere. In the later stage of corrosion, the corrosion of aluminium alloy did not become serious. Specifically, no obvious intergranular corrosion was found, which is related to the thermal treatment method. Corrosion products included Al(OH)₃, Al₂O₃, and AlCl₃, of which Al(OH)₃ is the most notable.

The 7xxx-series aluminum alloys are widely used in aircrafts due to their superior performance. The evolution of the mechanical properties of the aluminum alloys caused by marine atmospheric corrosion has become a research hotspot due to the increase in aircraft service time in the marine atmospheric environment. In this work, the evolution of the mechanical properties of the 7B04-T74 aluminum alloy was studied by an alternate immersion test. The surface microstructure was analyzed by SEM, EDS, XRD, and OM. The influence of the marine atmospheric corrosion on mechanical properties was studied by tensile and fatigue tests. The results show that the 7B04-T74 aluminum alloy has good corrosion resistance, as only pitting corrosion occurs in the marine atmospheric environment. The tensile properties of the 7B04-T74 aluminum alloy remained fundamentally the same before and after corrosion. The fatigue properties of the 7B04-T74 aluminum alloy were severely reduced, but the localized pitting corrosion only affected the initiation stage of the crack and had little effect on the crack propagation process.

Real-time monitoring and prediction of damages form the basis of Artificial Intelligence for IT Operations (AIOps) in mechanical equipment, relying on the Internet of Things (IoT). Acoustic emission technology is widely used in Prognostic and Health Management (PHM) to monitor the growth of fatigue cracks online. Extracting and selecting high-quality acoustic emission features are crucial to the accuracy of fatigue crack prediction, as it helps establish the relationship between these features and fatigue crack growth (FCG). However, traditional artificially selected acoustic emission features are seriously affected by the signal amplitude threshold. To solve the above problems, we proposed a fatigue crack prediction model based on the improved stacked autoencoder and attention mechanism (SSAE-AM). The model can adaptively extract acoustic emission features that are strongly correlated with FCG by adding a supervision module to the stacked autoencoder (SAE) and using the attention mechanism (AM)to weight the fusion features. On this basis, the relationship model between acoustic emission features and FCG is established for crack prediction. Finally, we verify the validity of the model through experiments that monitor fatigue crack growth under different loading stresses. Compared with models that use other acoustic emission statistical features for crack prediction, the model proposed in this paper can achieve better prediction accuracy.