Patrick E. Leser

IWSHM 2015: Probabilistic fatigue damage prognosis using surrogate models trained via three-dimensional finite element analysis

Utilizing inverse uncertainty quantification techniques, structural health monitoring (SHM) can be integrated with damage progression models to form a probabilistic prediction of a structure’s remaining useful life (RUL). However, damage evolution in realistic structures is physically complex. Accurately representing this behavior requires high-fidelity models which are typically computationally prohibitive. In this paper, high-fidelity fatigue crack growth simulation times are reduced by three orders of magnitude using a model based on a set of surrogate models trained via three-dimensional finite element analysis. The developed crack growth modeling approach is experimentally validated using SHM-based damage diagnosis data. A probabilistic prediction of RUL is formed for a metallic, single-edge notch tension specimen with a fatigue crack growing under mixed-mode conditions.

Multi-mode reverse time migration damage imaging using ultrasonic guided waves

HighlightsThe aim of the present work is to develop a multi‐mode imaging technique that will allow for identification of damage size and location using ultrasonic guided waves.The proposed technique combines a reverse‐time migration (RTM) imaging algorithm with a 3D wave propagation simulator using different wave modes.This combination enables the separation of multiple modes using the wavefield filtering techniques, potentially providing more information to damage types.Without the limitation of generating single dominant mode waves, wide frequency ranges are accessible, enabling optimal frequencies for a variety of ultrasonic data acquisition systems. ABSTRACT The sensitivity of Lamb wave modes to a particular defect or instance of damage is dependent on various factors (e.g., the local strain energy density due to that wave mode). As a result, different modes will be more useful than others for damage detection and quantification, dependent on damage type and location. For example, prior work in the field has shown that out‐of‐plane modes may have a higher sensitivity than in‐plane modes to surface defects in plates. The excitability of a certain data acquisition system and the corresponding resolution for damage imaging also varies with frequency. The aim of the present work was to develop a multi‐mode damage imaging technique that enables characterization of damage type and size, general sensitivity to unknown damage types, higher resolution imaging, and detectability regardless of the data acquisition system used. A reverse‐time migration (RTM) imaging algorithm was combined with a numerical simulator—the three‐dimensional (3D) elastodynamic finite integration technique (EFIT)—to provide multi‐mode damage imaging. The approach was applied to two simulated case studies featuring damaged isotropic plates. Sensitivities of damage type to wave mode were investigated by separating the Symbol and Symbol Lamb wave modes obtained from the resultant RTM wavefields. Symbol. No Caption available. Symbol. No Caption available.

IWSHM 2017: damage-scattered wave extraction in an integral stiffened isotropic plate: a baseline-subtraction-free approach

Ultrasonic guided waves enable long-distance inspection of structures for health monitoring purposes. However, this capability is diminished when applied to complex structures where damage-scattered waves are often buried by scattering from various structural components or boundaries in the time–space domain. Here, a baseline-subtraction-free inspection concept based on the Radon transform is proposed to identify and separate these scattered waves from those scattered by damage. The received time–space domain signals can be converted into the Radon domain, in which the scattered signals from structural components are suppressed into relatively small regions such that damage-scattered signals can be identified and extracted. In this study, a piezoelectric wafer and a linear scan via laser Doppler vibrometer were used to excite and acquire the Lamb wave signals in an aluminum plate with multiple stiffeners. Linear and inverse linear Radon transform algorithms were applied to the direct measurements. Currently, this method needs baseline measurements for comparison in the Radon domain, but avoids baseline subtraction. The results demonstrate the effectiveness of the Radon transform as an extraction tool for damage-scattered waves in a stiffened aluminum plate for a damage site in the bay area between two stiffeners and also suggest the possibility of generalizing this technique for application to a wide variety of complex, large-area structures.

A Diagnosis-Prognosis Feedback Loop for Improved Performance Under Uncertainties

The feed-forward relationship between diagnosis and prognosis is the foundation of both aircraft structural health management and the digital twin concept. Measurements of structural response are obtained either in-situ with mounted sensor networks or offline using more traditional techniques (e.g., nondestructive evaluation). Diagnosis algorithms process this information to detect and quantify damage and then feed this data forward to a prognostic framework. A prognosis of the structures future operational readiness (e.g., remaining useful life or residual strength) is then made and is used to inform mission- critical decision-making. Years of research have been devoted to improving the elements of this process, but the process itself has not changed significantly. Here, a new approach is proposed in which prognosis information is not only fed forward for decision-making, but it is also fed back to the forthcoming diagnosis. In this way, diagnosis algorithms can take advantage of a priori information about the expected state of health, rather than operating in an uninformed condition. As a feasibility test, a diagnosis-prognosis feedback loop of this manner is demonstrated. The approach is applied to a numerical example in which fatigue crack growth is simulated in a simple aluminum alloy test specimen. A prognosis was derived from a set of diagnoses which provided feedback to a subsequent set of diagnoses. Improvements in accuracy and a reduction in uncertainty in the prognosis- informed diagnoses were observed when compared with an uninformed diagnostic approach.

Probabilistic Fatigue Damage Prognosis Using a Surrogate Model Trained Via 3D Finite Element Analysis

Utilizing inverse uncertainty quantification techniques, structural health monitoring data can be integrated with damage progression models to form probabilistic predictions of a structure’s remaining useful life. However, damage evolution in realistic structures is physically complex. Accurately representing this behavior requires high-fidelity models which are typically computationally prohibitive. In this paper, high-fidelity fatigue crack growth simulation times are significantly reduced using a surrogate model trained via finite element analysis. The new approach is applied to experimental damage diagnosis data to form a probabilistic prediction of remaining useful life for a test specimen under mixed-mode conditions. doi: 10.12783/SHM2015/299