An advanced Wigner-Ville time-frequency analysis of lamb waves signals based upon AR model for efficient damage inspection

The generation and acquisition of the ultrasonic guided wave in metallic or composite structures to investigate the structural defects are quite straightforward; however, the interpretation and evaluation of the reflected/transmitted signal to extract the useful information is a challenging task. It is primarily due to the dispersion, and multi-modal behavior of the Lamb waves which is dependent on the exciting wave frequency and thickness of the material under investigation. These multi-modes and dispersion behavior lead to a complex waveform structure, and therefore, require an advanced signal processing technique to decipher the useful information in time and frequency domain. For this purpose, Wigner-Ville Distribution, due to its desirable mathematical properties, is considered as a powerful tool for estimating temporal and spectral features of this type of complex signals. However, because of its quadratic nature, the undesirable cross-terms and spurious energies are also generated, which limit the readability of the spectrum. To suppress this effect, the autoregressive model based upon Burg's Maximum Entropy method was employed that modified the kernels of the discrete Wigner-Ville Distribution. This technique was applied to ultrasonic Lamb wave signals, obtained numerically and experimentally, to extract useful discriminating spectral and temporal information that was required for mode identification, damage localization, and its quantification. For damage localization, based upon excellent time-frequency energy distribution, the proposed method precisely estimated the distance between two closely spaced notches in a plate from different simulated noisy signals with a maximum uncertainty of 5%. Moreover, time-frequency energy concentration in a combination with variation of its instantaneous frequency was also effective in identifying the overlapping modes of the Lamb wave signal. Lastly, for damage quantification, three time-frequency based damage indices namely, energy concentration, time-frequency flux, and instantaneous frequency were extracted from the five sets of specimens using the proposed time-frequency scheme and trained them for the regression model. The model testing proved that the damage indices has the potential to predict the crack sizes precisely and reliably

Deposition and removal studies of asphaltene from the glass surface

Asphaltene deposition in crude oil reservoirs and transportation lines affects the oil recovery and incurs additional operational costs. The current study discusses the removal of asphaltene from inorganic silica surfaces using hydrodynamic forces. The deposition of asphaltene was carried out on clean glass slides (proxy material for silica) from heptane-asphaltene dispersions via aging. For the removal of asphaltene, a parallel plate channel is fabricated with a pocket to place aged substrates under varying shear rates. The apparatus enables studying the surface morphology changes on a glass slide due to controlled flow conditions through physical contact techniques like atomic force microscopy (AFM). AFM characterizes the extent of both deposition and removal of asphaltene from the surface. The results show that large aggregates of asphaltene are removed from the surface with an increase in flow rates. The extent of removal of asphaltene from the substrate as a function of shear rate is determined. The study also discusses the possible mechanism of asphaltene removal from the surface using the hydrodynamic force calculations. The colloidal interactions calculated from hydrodynamic forces are reported to be Fadh/(d/2) = 1.29 mN/m. The presence of asphaltene tends to alter surface wettability. Interestingly, the contact angle measurements carried out on the asphaltene-deposited glass slides and after removal of asphaltene from the surface showed a negligible change, indicating incomplete removal of asphaltene from the surface

Lamb wave damage severity estimation using ensemble-based machine learning method with separate model network

Lamb wave-based damage estimations have great potential for structural health monitoring. However, designing a generalizable model that predicts accurate and reliable damage quantification result is still a challenge due to complex behavior of waves with different damage severities. In recent years machine learning algorithms have been proven to be an efficient tool to analyze damage-modulated Lamb wave signals. In this study, ensemble-based machine learning algorithms are employed to develop a generalizable crack quantification model for thin metallic plates. For this, the scattering of Lamb wave signals due to different configurations of crack dimension and orientation is extensively studied. Various finite-element simulation signals representing distinct crack severities in terms of crack length, penetration, and orientation are acquired. Realizing that both temporal and spectral information of the signal is extremely important to damage quantification, three time-frequency-based damage-sensitive features, namely energy concentration, time-frequency flux, and coefficient of variance, are proposed. These damage features are extracted by employing smoothed-pseudo Wigner-Ville distribution. After that, data augmentation technique based on the spline-based interpolation is applied to enhance the size of the dataset. Eventually, these fully developed damage dataset is deployed to train ensemble-based machine learning models. Here, we propose a separate model network (SMN), in which different models are trained and then link together to predict new and unseen datasets. The performance of the proposed model is demonstrated by two cases: first, simulated data incorporated with high artificial noises and in the second scenario, experimental data in raw form are employed to test the model. Results indicate that the proposed framework has the potential to develop a general model that yields reliable results for crack estimation

From data to insight, enhancing structural health monitoring using physics-informed machine learning and advanced data collection methods

Owing to recent advancements in sensor technology, data mining, Machine Learning (ML) and cloud computation, Structural Health Monitoring (SHM) based on a data-driven approach has gained more popularity and interest. The data-driven methodology has proved to be more efficient and robust compared with traditional physics-based methods. The past decade has witnessed remarkable progress in ML, especially in the field of Deep Learning (DL) which are effective in many tasks and has achieved state-of-the-art results in various engineering domains. In the same manner, DL has also revolutionized SHM technology by improving the effectiveness and efficiency of models, as well as enhancing safety and reliability. To some extent, it has also paved the way for implementing SHM in real-world complex civil and mechanical infrastructures. However, despite all the success, DL has intrinsic limitations such as its massive-labelled data Requirement, inability to generate consistent results and lack of generalizability to out-of-sample scenarios. Conversely, in SHM, the lack of data corresponding to a different state of the structure is still a challenging task. Recent development in physics-informed ML methods has provided an opportunity to resolve these challenges in which limited-noisy data and mathematical models are integrated through ML algorithms. This method automatically satisfies physical invariants providing better accuracy and improved generalization. This manuscript presents the sate-of-the-art review of prevailing ML methods for efficient damage inspection, discuss their limitations, and explains the diverse applications and benefits of physics-informed ML in the SHM setting. Moreover, the latest data extraction strategy and the internet of things (IoT) that support the present data-driven methods and SHM are also briefly discussed in the last section.</p

A new approach for quantification of corrosion losses on steels exposed to an artificial seawater environment

The selection methodology for thickness loss measurement is very important to determine the extent of corrosion damage, as well as in formulation of corrosion prediction models and inspection/maintenance plans for offshore structures. This paper introduces a more accurate corrosion measurement technique, based on the pre-exposure dimensional metrology and post-exposure optical microscopy/image analysis on the cross-sections of steel samples. During this corrosion test, the surface grinded and uncoated steel samples were submerged vertically in an artificial seawater solution, for a duration of up to a maximum of 365 days. The corrosion damage experienced on the steel samples means that the dimensional metrology can be more accurate, and useful approach to measure both uniform and localised corrosion losses simultaneously than the conventional average mass loss method

Effect of pH and surfactants on shear induced asphaltene removal

Asphaltene removal from sediments is essential for enhanced oil recovery from heavier crude oil reservoirs and tar sands and bitumen recovery from bottom products in downstream processes. Water injection or water flooding at high pressures exert shear forces that can overcome the adhesive forces between asphaltene and mineral surfaces. The adhesive forces are also affected by ions in the aqueous medium. In the current work we study asphaltene removal from silica surface using shear forces of aqueous media in a parallel plate channel. We demonstrate the effect of varying pH and surfactant conditions in aqueous media on asphaltene removal efficiency. We relate the removal efficiency with fractional asphaltene volume on the surface estimated from atomic force microscopy. The fractional asphaltene volume reduces to 0.12 at pH 10, which is approximately 50% lower than water at neutral pH at the same shear rate. We show that the water-soluble anionic surfactants are inefficient in asphaltene removal, whereas cationic surfactant reduces the asphaltene fraction to 0.30. We conclude that the removal efficiency is affected by the zeta potential of the asphaltene and the surface, where electrostatic repulsion between the asphaltene and the surface and increased wettability in the presence of cationic surfactant improves asphaltene removal

PPAR-&#947; and CYP46A1 genes polymorphism is associated with Primary Open Angle Glaucoma (POAG) in hypertensive North Indians

Background: Involvement of genetic factors like gene polymorphisms was found to contributesignificantly to development and progression of Primary Open Angle Glaucoma (POAG) in the last few decades.Aim of study: The present study was carried out to investigate association of PPAR-γ (rs10865710) and CYP46A1 (rs754203) gene polymorphism with development of POAG in hypertensive North Indians.Patients and methods: Study included 328 individuals, 226 as POAG cases and 102 controls. PPAR-γ and CYP46A1 gene polymorphism was evaluated by polymerase chain reactionrestriction fragment length polymorphism (PCR-RFLP). The genotypic and allelic frequency distribution in patients and controls was analyzed by SPSS (version 12.0).Results: In CYP46A1 gene polymorphism, TT genotype and T allele were found to be associated with a significantly decreased risk of POAG whereas the CT, CC genotypes and C allele were associated with an increased risk of POAG in both hypertensive and normotensive individuals. In PPAR-γ gene polymorphism, only GG genotype was nearly associated with POAG in only hypertensive cases

PPAR-γ and CYP46A1 genes polymorphism is associated with Primary Open Angle Glaucoma (POAG) in hypertensive North Indians

Background: Involvement of genetic factors like gene polymorphisms was found to contribute significantly to development and progression of Primary Open Angle Glaucoma (POAG) in the last few decades. Aim of study: The present study was carried out to investigate association of PPAR-γ (rs10865710) and CYP46A1 (rs754203) gene polymorphism with development of POAG in hypertensive North Indians. Patients and methods: Study included 328 individuals, 226 as POAG cases and 102 controls. PPAR-γ and CYP46A1 gene polymorphism was evaluated by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). The genotypic and allelic frequency distribution in patients and controls was analyzed by SPSS (version 12.0). Results: In CYP46A1 gene polymorphism, TT genotype and T allele were found to be associated with a significantly decreased risk of POAG whereas the CT, CC genotypes and C allele were associated with an increased risk of POAG in both hypertensive and normotensive individuals. In PPAR-γ gene polymorphism, only GG genotype was nearly associated with POAG in only hypertensive cases. Conclusions: CYP46A1 (rs754203) gene polymorphism was associated with POAG in both hypertensive and normotensive patients whereas, only GG genotype of PPAR-γ (rs10865710) SNP shows significant association with POAG in hypertensive POAG patients

Evaluation of the influence of dissolved nitrates on corrosion behaviour of ship structural steel exposed to seawater environment

Corrosion rates in marine structural steels differ significantly with the varying compositions of seawater particularly near harbours or coastal regions primarily due to the presence of untreated chemically active species from various sources. The reviewed literature reports accelerated steel corrosion losses in coastal seawater exposure conditions, which has widely been attributed to the presence of aggressive chemical compounds e.g., dissolved inorganic nitrogenous (DINs) compounds, sulphur containing compounds, in combination with various other environmental factors and their interdependent complex relationships. This paper aims to investigate the influence of nitrates, a DIN compound, on the corrosion behaviour of a low carbon ship structural steel, by exposing surface the cleaned coupons to an artificial seawater solution in a controlled laboratory environment. The uniform and localised corrosion damages were measured on steel coupons by using the standard weight loss and the dimensional metrology methods. A significant increase in corrosion losses was observed on coupons exposed to the nitrate-added artificial seawater than those exposed to similar seawater compositions with no additional nitrate content. Elemental compositions of corrosion deposits and corrosion morphologies investigated using various analytical tools such as SEM, EDS and Raman scattering techniques have shown different types of corrosion products in both exposure conditions

Evaluation of the influence of dissolved nitrates on corrosion behaviour of ship structural steel exposed to seawater environment

peer reviewedCorrosion rates in marine structural steels differ significantly with the varying compositions of seawater particularly near harbours or coastal regions primarily due to the presence of untreated chemically active species from various sources. The reviewed literature reports accelerated steel corrosion losses in coastal seawater exposure conditions, which has widely been attributed to the presence of aggressive chemical compounds e.g., dissolved inorganic nitrogenous (DINs) compounds, sulphur containing compounds, in combination with various other environmental factors and their interdependent complex relationships. This paper aims to investigate the influence of nitrates, a DIN compound, on the corrosion behaviour of a low carbon ship structural steel, by exposing surface the cleaned coupons to an artificial seawater solution in a controlled laboratory environment. The uniform and localised corrosion damages were measured on steel coupons by using the standard weight loss and the dimensional metrology methods. A significant increase in corrosion losses was observed on coupons exposed to the nitrate-added artificial seawater than those exposed to similar seawater compositions with no additional nitrate content. Elemental compositions of corrosion deposits and corrosion morphologies investigated using various analytical tools such as SEM, EDS and Raman scattering techniques have shown different types of corrosion products in both exposure conditions