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Research Papers

ASME J Nondestructive Evaluation. 2019;2(2):021001-021001-6. doi:10.1115/1.4042871.

This paper describes a new concept to monitor the temperature of water utilizing the acoustic resonance, which occurs when ultrasound passes through a thin layer. In the ultrasonic transmission system that comprises of the reflection plate, thin film, and water, the reflection coefficient of the ultrasound at the plate/film/water interface depends on the frequency and takes its minimum value at the resonant frequency. Notably, this is closely related to the acoustic impedance of the water; moreover, it is a known fact that the acoustic impedance of the water demonstrates temperature dependence. Against this background, the present study aims to develop a technique in order to monitor the temperature of water utilizing the aforementioned correlation between the reflection coefficient and water temperature. First, a theoretical model was developed to determine the acoustic impedance of water from the difference in the amplitude spectra of echoes reflected at the back of the plate in the cases both with and without the film. It was found that the ratio of the amplitude spectrum of the echo recorded in the case with the film to that in the case without the film clearly decreased with a drop in water temperature. From this, we obtained the equation for determining water temperature experimentally. Finally, the temperature of water, which was brought down by air or ice cooling, was monitored by the proposed method. It was found that the behavior of temperature determined by the proposed method was congruent with that which was measured by a thermocouple.

Commentary by Dr. Valentin Fuster
ASME J Nondestructive Evaluation. 2019;2(2):021002-021002-10. doi:10.1115/1.4043122.

In this paper, the time-varying autoregressive (TVAR) model is integrated with the K-means—clustering technique to detect the damage in the steel moment-resisting frame. The damage is detected in the frame using nonstationary acceleration response of the structure excited using ambient white noise. The proposed technique identifies and quantifies the damage in the beam-to-column connection and column-to-column splice plate connection caused due to loosening of the connecting bolts. The algorithm models the nonstationary acceleration time history and evaluates the TVAR coefficients (TVARCs) for pristine and damage states. These coefficients are represented as a cluster in the TVARC subspace and segregated and classified using K-means—segmentation technique. The K-means—approach is adapted to simultaneously perform partition clustering and remove outliers. Eigenstructure evaluation of the segregated TVARC cluster is performed to detect the temporal damage. The topological and statistical parameters of the TVARC clusters are used to quantify the magnitude of the damage. The damage is quantified using the Mahalanobis distance (MD) and the Itakura distance (ID) serving as the statistical distance between the healthy and damage TVARC clusters. MD calculates a multidimensional statistical distance between two clusters using the covariance between the state vectors, whereas ID measures the dissimilarity of the autoregressive (AR) parameter between reference state and unknown states. These statistical distances are used as damage-sensitive feature (DSF) to detect and quantify the initiation and progression of the damage in the structure under ambient vibrations. The outcome of both the DSFs corroborate with the experimental investigation, thereby improving the robustness of the algorithm by avoiding false damage alarms.

Topics: Damage , Algorithms , Steel
Commentary by Dr. Valentin Fuster
ASME J Nondestructive Evaluation. 2019;2(2):021003-021003-11. doi:10.1115/1.4043276.

The current techniques in assessing the healing of a fixated fractured long bone, which include X-ray, computed tomography (CT), and manual manipulation, are qualitative and its accuracy depends on the surgeon's experience. A lack of a robust and quantitative monitoring method of fractured bone healing limits the survival of orthopedic implants and the ability to accurately predict and prevent fixation failure and complications. This paper experimentally and computationally investigates the efficacy and the potential application of a vibration-based quantitative monitoring methodology. This nonintrusive technique incorporates the cross-spectra response of externally placed sensors located remotely from the fractured region. In this study, the test specimens are composite femurs fixated with an intramedullary nail fixation system and the epoxy adhesive applied in the osteotomized region is used to simulate the healing process. Epoxies with a 30-min and 2 h gel time are used separately to investigate the sensitivity of this healing assessment technique. The findings highlight the key vibrational modes to establish the state of healing and the quantification evaluation of healing of fixated femurs based on a formulated healing index is also presented. This efficacy study seeks to verify the viability of this external measurement technique for human health monitoring and the future development of healing devices.

Commentary by Dr. Valentin Fuster
ASME J Nondestructive Evaluation. 2019;2(2):021004-021004-10. doi:10.1115/1.4043503.

This paper summarizes a data fusion approach for utilizing conventional lubrication parameters in an unconventional method for identifying deterioration in a thermally coupled system. Complex machines are composed of multiple systems that are intrinsically dependent. Design of these systems requires expertise in distinct disciplines with a determined focus on meeting system-specific requirements. This expertise focused approach promotes a silo mindset to system design, which is then carried through to the design and implementation of the health management system of these machines. These multidisciplinary interacting systems are traditionally monitored as independent entities, with little advantage taken of the direct and cross-coupled effects. For example, parameters required for lubrication health monitoring include, but are not limited to, oil pressure and temperature. These parameters are critical in determining the health of the lubrication system. However, how these parameters change can be an indicative of the health of interacting systems otherwise considered independent and isolated. By exploring the rationale of the cross-system impacts, physical interactions between these systems (albeit empirical knowledge) can be used for cross-system monitoring. A means of achieving this objective is to utilize parameters that are measured in one system to determine the diagnostic state of another coupled system with limited, or no, system observability. A fuzzy logic fusion approach is employed in this task and was designed and implemented for the above-mentioned purpose. The focus of interest was on the lubrication and hot section interactions with parameters obtained from real machines. Fuzzy membership functions and rules were determined and tuned appropriately from real data and applied to nominal and defective machines.

Commentary by Dr. Valentin Fuster
ASME J Nondestructive Evaluation. 2019;2(2):021005-021005-7. doi:10.1115/1.4043605.

Elastodynamic Green's function for anisotropic solids is required for wave propagation modeling in composites. Such modeling is needed for the interpretation of experimental results generated by ultrasonic excitation or mechanical vibration-based nondestructive evaluation tests of composite structures. For isotropic materials, the elastodynamic Green’s function can be obtained analytically. However, for anisotropic solids, numerical integration is required for the elastodynamic Green's function computation. It can be expressed as a summation of two integrals—a singular integral and a nonsingular (or regular) integral. The regular integral over the surface of a unit hemisphere needs to be evaluated numerically and is responsible for the majority of the computational time for the elastodynamic Green's function calculation. In this paper, it is shown that for transversely isotropic solids, which form a major portion of anisotropic materials, the integration domain of the regular part of the elastodynamic time-harmonic Green's function can be reduced from a hemisphere to a quarter-sphere. The analysis is performed in the frequency domain by considering time-harmonic Green's function. This improvement is then applied to a numerical example where it is shown that it nearly halves the computational time. This reduction in computational effort is important for a boundary element method and a distributed point source method whose computational efficiencies heavily depend on Green's function computational time.

Commentary by Dr. Valentin Fuster

Technical Brief

ASME J Nondestructive Evaluation. 2019;2(2):024501-024501-6. doi:10.1115/1.4043191.

Failure of the rotary seal is one of the foremost causes of breakdown in rotary machinery, and such a failure can be catastrophic, resulting in costly downtime and large expenses. Assessing the performance degradation of the rotary seal is very important for maintenance decision-making. Although significant progress has been made over the last 5 years to understand the degradation of seals using experimental verification and numerical simulation, there is a research gap on the data-driven-based tools and methods to assess the health condition of rotary seals. In this paper, we propose a data-driven-based performance degradation assessment approach to classify the running/health condition of rotary seals, which was not considered in the previous studies. Statistical time domain features are extracted from friction torque-based degradation signals collected from a rotary setup. Wrapper-based feature selection approach is used to select relevant features, with multilayer perceptron neural network utilized as a classification technique. To validate the proposed methodology, an accelerated aging and testing procedure is developed to capture the performance of rotary seals. The study findings indicate that multilayer perceptron (MLP) classifier built using features related to the amplitude of torque signal has a better classification accuracy for unseen data when compared with logistic regression and random forest classifiers.

Commentary by Dr. Valentin Fuster

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