Literatura académica sobre el tema "SHM-POD"

Optical fiber sensors (OFSs) represent an efficient sensing solution in various structural health monitoring (SHM) applications. However, a well-defined methodology is still missing to quantify their damage detection performance, preventing their certification and full deployment in SHM. In a recent study, the authors proposed an experimental methodology to qualify distributed OFSs using the concept of probability of detection (POD). Nevertheless, POD curves require considerable testing, which is often not feasible. This study takes a step forward, presenting a model-assisted POD (MAPOD) approach for the first time applied to distributed OFSs (DOFSs). The new MAPOD framework applied to DOFSs is validated through previous experimental results, considering the mode I delamination monitoring of a double-cantilever beam (DCB) specimen under quasi-static loading conditions. The results show how strain transfer, loading conditions, human factors, interrogator resolution, and noise can alter the damage detection capabilities of DOFSs. This MAPOD approach represents a tool to study the effects of varying environmental and operational conditions on SHM systems based on DOFSs and for the design optimization of the monitoring system.

Condition-based maintenance refers to the installation of permanent sensors on a structure/system. By means of early fault detection, severe damage can be avoided, allowing efficient timing of maintenance works and avoiding unnecessary inspections at the same time. These are the goals for structural health monitoring (SHM). The changes caused by incipient damage on raw data collected by sensors are quite small, and are usually contaminated by noise and varying environmental factors, so the algorithms used to extract information from sensor data need to focus on sensitive damage features. The developments of SHM techniques over the last 20 years have been more related to algorithm improvements than to sensor progress, which essentially have been maintained without major conceptual changes (with regards to accelerometers, piezoelectric wafers, and fiber optic sensors). The main different SHM systems (vibration methods, strain-based fiber optics methods, guided waves, acoustic emission, and nanoparticle-doped resins) are reviewed, and the main issues to be solved are identified. Reliability is the key question, and can only be demonstrated through a probability of detection (POD) analysis. Attention has only been paid to this issue over the last ten years, but now it is a growing trend. Simulation of the SHM system is needed in order to reduce the number of experiments.

Purpose – A design of sensor networks for structural health monitoring (SHM) with guided waves poses a hard challenge. Therefore different approaches are possible. A known one is the usage of probability of detection (POD) criteria. Here, areas of potential impact sensitivity are calculated for every sensor which leads to a POD. The number of sensors is increased until a demanded POD is reached. However, these calculations are usually based on finite element methods and underlie different assumptions and approximations which can cause different inaccuracies. These limitations are avoided by using an experimental data basis for virtual sensors in this paper. The paper aims to discuss these issues. Design/methodology/approach – An air-coupled ultrasound scanning technique is used for guided wave investigations. Recorded displacements of a structure surface are used as stimulation of virtual sensors which can be designed by software and positioned within available data field. For the calculation of sensor signals an isogeometric finite element model is used. The virtually bonded layer of the virtual piezoceramic sensor interpolates with non-uniform rational B-Splines (NURBS) the measured nodal data for each time step. This interpolation corresponds to a displacement boundary condition and is used to calculate the electrical potential at the free surface of the sensor. Findings – Experimental data based on air-coupled ultrasound scanning technique can be used for elimination of disadvantages in numerical simulations by developing sensor networks for SHM. In combination with a transfer matrix method (TM) a three-dimensional displacement of specimen surface for complex composites can be calculated. To obtain the sensor signal a surface-bonded sensor is modeled by an isogeometric finite element approach. A good accordance is found between calculated virtual sensor signal and its experimental verification. Research limitations/implications – Some deviations between calculated signal and its experimental verification are mainly justified by different spectral transfer functions between wave field scanning technique and signal recording of applied sensors. Furthermore, sensor influence on wave propagation is neglected in the presented method. Originality/value – In this paper, the principle of virtual sensors is applied on anisotropic multilayered lamina by using isogeometric finite elements for piezoelectric sensors. This enables any sensor dimension, layout and position on complex composites. Furthermore a bonding layer between specimen and sensor is considered. The method allows a detailed analysis of sensor behavior on a specimen surface and the design and optimization of entire sensor networks for SHM.

Within model-based approaches to structural health monitoring (SHM), numerical simulations must be tailored to continuously adapt to the degradation processes and to the possibly changing environment. This model update stage of the analysis brings two competing requirements: the accuracy of the model, with a more detailed description of the phenomena required where damage is supposed to take place; the efficiency of the model, to reduce the overall computational burden and allow for real-time (or close to real-time) computing. Without resorting to AI-based strategies, approaches solely based on proper orthogonal decomposition (POD) and domain decomposition (DD) techniques proved rather efficient in handling the aforementioned trade-off between the diverging requirements of accuracy and efficiency. In this work, we discuss a further improvement over our recently proposed methodology that consists of: a DD of the entire structure into sub-regions, which can be designed to decouple regions more prone to get damaged from regions that are instead less affected by the degradation processes; a POD-based selective model order reduction for all the domains, with adjustable and heterogeneous accuracy requirements. The approach is assessed through an illustrative example related to beam dynamics, with results provided in terms of both accuracy and computational efficiency, or speedup with respect to the full-order model.

U radu je opisan značaj primene seznora u mernim uređajima kroz primer jedne bitne oblasti u građevinarstvu poznate pod nazivom „Praćenje stanja građevinskih konstrukcija“ (eng. Structural health monitoring - SHM). Takođe, opisan je i sistem koji je realizovan sa ciljem da obezbedi neophodna merenja. Merene su vibracije, uz ređe praćenje temperature. Postoji i mogućnost alarmiranja korisnika u slučaju kritičnih vibracija, filtracija podataka, računanje FFT-a, snimanje dobijenih rezultata, njihovo otvaranje i obrada u nekom od drugih softverskih paketa. Sistem je realizovan unutar LabVIEW programskog okruženja. Višeosni akcelerometar PCB-356A32 je senzor zadužen za merenje vibracija, dok je termopar tipa K korišten kao senzor temperature.

The capabilities of detection and localization of damage in a structure, using a guided wave-based structural health monitoring (GWSHM) system, depend on the damage location and the chosen sensor array setup. This paper presents a novel approach to assess the reliability of an SHM system enabling to quantify localization accuracy. A two-step technique is developed to combine multiple paths to generate one probability of detection (POD) curve that provides information regarding the detection capability of an SHM system at a defined damage position. Moreover, a new method is presented to analyze localization accuracy. Established probability-based diagnostic imaging using a signal correlation algorithm is used to determine the damage location. The resultant output of the localization accuracy analysis is the smallest damage size at which a defined accuracy level can be reached at a determined location. The proposed methods for determination of detection probability and localization accuracy are applied to a plate-like CFRP structure with an omega stringer with artificial damage of different sizes at different locations. The results show that the location of the damage influences the sensitivity of detection and localization accuracy for the used detection and localization methods. Localization accuracy is enhanced as it becomes closer to the array’s center, but its detection sensitivity deteriorates.

The performance and reliability of Structural Health Monitoring (SHM) techniques remain largely unquantified. This is in contrast to the probability of detection (POD) and sensitivity of manual non destructive inspection methods which are well characterised. In this study factors influencing the rates of emission of Acoustic Emission (AE) signals from propagating fatigue cracks were investigated. Fatigue crack growth experiments were performed in 2014 T6 aluminium sheet to observe the effects of changes in crack length, loading spectrum and sample geometry on rates of emission and the probability of detecting and locating the fatigue crack. Significant variation was found in the rates of AE signal generation during crack progression from initiation to final failure. AE signals at any point in the failure process were found to result from different failure mechanisms operating at particular stages in the failure process.

Acoustic emission (AE) techniques have obvious attractions for structural health monitoring (SHM) due to their extreme sensitivity and low sensor density requirement. A factor preventing the adoption of AE monitoring techniques in certain industrial sectors is the lack of a quantitative deterministic model of the AE process. In this paper, the development of a modular AE model is described that can be used to predict the received time-domain waveform at a sensor as a result of an AE event elsewhere in the structure. The model is based around guided waves since this is how AE signals propagate in many structures of interest. Separate modules within the model describe (a) the radiation pattern of guided wave modes at the source, (b) the propagation and attenuation of guided waves through the structure, (c) the interaction of guided waves with structural features and (d) the detection of guided waves with a transducer of finite spatial aperture and frequency response. The model is implemented in the frequency domain with each element formulated as a transfer function. Analytic solutions are used where possible; however, by virtue of its modular architecture it is straightforward to include numerical data obtained either experimentally or through finite element analysis (FEA) at any stage in the model. The paper will also show how the model can used, for example, to produce probability of detection (POD) data for an AE testing configuration.

Chickpea is a major pulse crop which is grown and consumed by the Indian people. Due to poor weed competition ability of chickpea and very few weed management options, yield of chickpea is drastically reduced under vertisols. Therefore, the present study related to weed management and crop residues on weed incidence, yield attributing character and yield of chickpea was conducted at Research Farm of JNKVV, Jabalpur (MP), India. A field experiment was undertaken in split plot design with 3 replications and 4 weed-management treatment in main-plot and four crop residues as sub-plot. The main plot treatments were pendimethalin 38.7% CS at 1 kg/ha as pre plant incorporation (PPI), hand weeding at 30 days after sowing (DAS), hand hoeing at 30 DAS and weedy check. Four crop residues mulch (CRM) were, wheat straw (WSM), paddy straw (PSM) and soybean haulm (SHM) each at 5 t/ha and control where no mulch material was applied. Results revealed that imposition of hand weeding at 30 DAS recorded with least weeds with lesser weed biomass resulting in higher weed control efficiency (WCE). However, weedy check recorded maximum weed count and dry weight. Pendimethalin 1 kg/ha recorded lower weed prevalence and weed dry weight. It was similar to hand hoeing done at 30 DAS. Among applied CRM, PSM recorded lower weed density and dry weight with higher WCE and soil moisture at 30 DAS and was superior over control plots. Hand weeding at 30 DAS recorded with higher yield attributing traits viz, pods/plant, seed/pod and seed index resulted higher seed yield (1,604 and 1,731 kg/ha respectively in 2018–19 and 2019–20). It was at par with pendimethalin at 1 kg/ha. The lower yield attributes and yield was recorded in weedy check plots. Among CRM, spreading of PSM give more pods and seeds/pod with higher seed index resulted in higher seed yield (1515 and 1593 kg/ha in 2018–19 and 2019–20 respectively) over others. Thus, application of PSM at 5 t/ha with one hand weeding at 30 DAS or with pendimethalin can be suggested for significant weed control and higher seed yield in chickpea.

This paper describes, for the first time, the application of an Elastodynamic Boundary Element Method (BEM) in Laplace Domain for the Structural Health Monitoring (SHM) of poly-crystalline materials. The study focuses on Ultrasonic Guided Wave (UGW) propagation and investigates the wave–material interactions at micro-scale. The study aims to investigate the interaction of UGWs with assessing micro-structural features such as grain size, morphology, degradation, and flaws. Numerical simulations of the most common micro-structural features demonstrate the accuracy and validity of the proposed method. Particular attention is paid to the study of porosity and its influence on material macro-properties. Different crystal morphologies such as cubic, rhombic, and truncated octahedral are considered. The detection of voids based on the changes in the amplitude and Time of Arrival (ToA) of the backscattered signal is investigated. The study also considers inter-granular cracks, which cause laceration, and examines flaw position/orientation, length, and distance from a specific reference. Furthermore, a framework is proposed for generating Probability of Detection (PoD) curves using numerical simulations. Experimental tests in pristine conditions are shown to be in good agreement with the numerical simulations in terms of ToA, signal amplitude, and wave velocity. The numerical simulations provide insights into wave propagation and wave–material interactions, including different types of defects at the micro-scale. Overall, the BEM and UGW methods are shown to be effective tools for better understanding micro-structural features and their influence on the macro-structural properties of poly-crystalline materials.

Les ondes élastiques guidées émises et reçues par des transducteurs piézoélectriques minces sont reconnues comme une technologie prometteuse pour plusieurs applications de surveillance de l'état de santé des structures (ou Structural Health Monitoring - SHM), en particulier pour les composants aérospatiaux. La démonstration des performances de ces systèmes, souvent exprimées en termes de courbe de probabilité de détection (POD), est un élément clé du déploiement réussi de cette technologie dans l'industrie. La détermination expérimentale de la courbe POD nécessite de nombreux échantillons instrumentés, ce qui rend son coût prohibitif. Une approche basée sur la simulation, ou assistée par un modèle, est une alternative intéressante. Cependant, la simulation de systèmes SHM basés sur les ondes guidées et la détermination de la courbe POD de tels systèmes sont jusqu'à présent limitées en raison d'un manque de méthodologie spécifique, de procédures, de méthodes statistiques appropriées et de validation. Cette thèse propose une méthodologie générale pour une approche POD assistée par la simulation de système SHM par ondes guidées, avec une démonstration sur la surveillance d'une fissure croissante à partir d'un trou dans une plaque d'aluminium. La méthodologie tire parti de l'outil de simulation par éléments finis spectraux transitoires dans le domaine temporel développé au CEA-List (module CIVA SHM) qui permet d'exécuter les grandes campagnes de simulation nécessaires pour déterminer une courbe POD. Un nouveau modèle d'actionneur hybride a été proposé dans ce travail en considérant le comportement dépendant de la fréquence du transducteur et la contrainte normale en plus de la contrainte radiale comme charges surfaciques afin de permettre l’utilisation de la simulation sur une plus grande gamme de fréquences d'excitation, adaptées à l'application visée. Deux méthodes récentes et appropriées d’un point de vue statistique : la « longueur à la détection » et « random effects », ont ensuite été adaptées pour estimer et comparer la courbe POD à partir des ensembles de données expérimentales et simulées. L'approche bayésienne s'est avérée plus utile que l'estimation du maximum de vraisemblance pour l'estimation des paramètres du modèle de la méthode random effects afin de comparer la limite d'incertitude pour chaque paramètre du modèle à partir des ensembles de données expérimentales et simulées. Enfin, une étude de détermination de la taille de l'échantillon a été menée sur la base de la méthode random effects afin d'identifier le nombre d'échantillons nécessaires pour répondre aux exigences d'une application SHM particulière. Tous ces résultats montrent une grande confiance dans l'approche assistée par la simulation pour l’estimation de la POD et confirment le potentiel de cette solution en tant qu'outil compatible avec les exigences industrielles pour la démonstration des performances des systèmes SHM basés sur les ondes guidées
Guided elastic waves emitted and received by thin piezoelectric transducers are recognized as a promising technology for several applications of Structural Health Monitoring, especially of aerospace components. Demonstration of the performances of such systems, often expressed in terms of Probability Of Detection (POD) curve, is a key enabler of the successful deployment of the technology in industry. POD curve experimental determination requires many instrumented samples making its cost prohibitive. A simulation-based approach, or model-assisted, is an attractive alternative. However, simulation in guided waves-based SHM and POD determination of such systems are so far limited due to a lack of specific methodology, procedures, appropriate statistical methods, and validation. This thesis proposes a general methodology for a model-assisted POD approach of guided waves based SHM, with a demonstration on monitoring of a growing crack from a hole in an aluminum plate. The methodology benefits from the efficient time domain transient spectral finite element simulation tool developed at CEA-List (CIVA SHM module) that allows to run the large simulation campaigns required to determine a POD curve. A new hybrid actuator model has been proposed in this work by considering the transducer frequency dependent behaviour and normal stress in addition to radial stress as a surface loads to enable the use of simulation on a higher range of excitation frequencies, suitable for the targeted application. Two recent suitable statistical methods: length-at-detection and random effects, have then been adapted to estimate and to compare the POD curve from both experimental and simulated datasets. The Bayesian approach is found to be more useful in model parameter estimation of random effects method for comparing the uncertainty bound for each model parameter from experimental and simulated datasets than Maximum Likelihood Estimation. Finally, a sample size determination study has been conducted based on the random effects method to identify how many samples are required to achieve the requirement of a particular SHM application. All these results show great confidence in the model-assisted approach to POD estimation methodology and confirm the potential of this solution as a cost-effective tool for performance demonstration of guided waves-based SHM systems

Abstract The Federal Aviation Administration (FAA) has been participating with the Society of Automotive Engineers (SAE) Aerospace Industry Steering Committee (AISC) to develop a methodology for calculating the Probability of Detection (POD) for Structural Health Monitoring (SHM) for damage detection on commercial aviation. Two POD methodologies were developed: one by Dr. William Meeker, Iowa State University, and the other by Dennis Roach, Sandia National Laboratories (SNL). With Dr. Seth Kessler, Metis Design Corp, a test program of 24 samples of aluminum strips to be fatigued on MTS machines was developed. The samples were designed to meet the ASTM E647. Twelve samples had two SHM modalities on the front and back from Metis (PZT and carbon nanotubes), and the other twelve had SHM sensors from Structural Monitoring Systems (SMS) (comparative vacuum monitoring – CVM) and Acellent Technologies (PZT). The tests were performed at the FAA William J Hughes Technical Center in Atlantic City, NJ. The samples were cycled every 1500 cycles and then stopped for SHM data collection. Once the crack exceeded 0.125 inches and provided for a minimum of 15 inspections, a new sample was tested until all 12 samples were completed. The data was provided to each company to be set up in the format needed to run through the POD methodologies. Then the data was provided to Dr. Meeker and Dr. Roach for analysis. This paper will provide the results of those tests.

This paper explores a novel approach to establishing detection sensitivity for SHM. The proposed approach uses data associated with the largest flaw such that all smaller flaws were not detected and the smallest flaw such that all larger flaws were detected for that specimen. These points are termed “limit defining” because they directly contribute to defining the most valuable portion of the probability of detection (POD) curve. A POD curve can be efficiently built using a standard hit/miss analysis, and an upper confidence bound can be generated by calculating the Covariance of the Limit Defining Pair (CLDP) to take the dependence into account. A fatigue experiment was conducted on 60 aluminum bars using a carbon nanotube (CNT) based fatigue crack gauge. 100 data points were collected for each specimen as the crack grew to ~2mm. 1000 classic POD curves were generated by randomly selecting 1 point from each specimen to observe variability, and subsequently the same data was analyzed using the CLDP. Finally, random subsets of specimens were selected to simulate POD statistics associated with testing fewer specimens. Overall, CPLD has proven to be a robust and reliable alternative to traditional detection sensitivity evaluation for SHM sensors.

Structural health monitoring (SHM) performs the function of evaluating performance and durability for structural life management by monitoring changes in engineering structures such as buildings and bridges. In order to obtain information about a structure's ability to perform its intended function, data collection activities are required through various inspections aimed at detecting the presence of structural damage. Repeated inspections have been proposed to increase the reliability of SHM. Many people considered repeated inspection as a way to increase the chance of detecting damage. If more than one of the individual inspections finds damage, collectively evaluates the damage as detected and produces the results of repeated inspections. Probability of detection (POD) was used as a measure of the sensitivity and reliability of the inspection process. To evaluate structural condition and predict remaining service life, POD is measured and structural life is calculated based on initial defect sizes that are just below the inspection limits of non-destructive testing techniques. Repeated inspections can be considered by multiplying the likelihood function, but if a single inspector performs repeated inspections, they may not be independent because they may be biased by previous inspection results. The repeated inspections is independent if performed by an automated system or another inspector unaware of previous inspection results. It can be assumed that each inspection is independent in that the SHM system can automatically collect data even in areas where general non-destructive testing is impractical due to complex geometries and accessibility limitations, but conversely, due to the dependencies of the data, there is no statistical difference between subsequent measurements. It is also considered to be less independent. In this paper, the effect of repeated inspection on POD improvement was confirmed using eddy current inspection data, and the benefits of repeated inspection differed from those predicted by assuming complete independence. Furthermore, the effectiveness of repeated inspection was discussed.

Detection of defects is of outmost importance for composite airframe due to barely visible damage which may appear at any time during lifetime. Several Structural Health Monitoring (SHM) technologies have been developed so far to continuously monitor the condition of the structure. Guided Ultrasonic Waves (GUW) are well suited for this application, but there is still a significant gap in reliability assessment and quantification of system performances. Although the theory of Probability of Detection (POD) can be used to this end, but the reliability assessment procedure needs to be properly addressed for each type of system. In this work different concepts are proposed, including two artificial intelligence approaches, tested and compared to enable a reliability assessment comparable to the conventional POD framework. On top, a novel approach on how to combine experimental data of the undamaged structure with simulations of the damaged structure is proposed. The proposed ideas are tested on a single carbon fiber composite specimen, and data is analyzed using all the different concepts. The results show that classic POD and AI-based reliability assessment can be compared as well as experimental and synthetic data can be used when the experimental variability is matched properly providing a paradigm shift in the reliability assessment field.

Abstract The American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code (BPVC) Committee has recently developed a new Section XI (Nuclear Components Inspection) Division 2 Code [1] named “Reliability and Integrity Management (RIM).” RIM incorporates a new concept known as “System-Based Code (SBC)” originally due to Asada and his colleagues [2, 3], where an integrated approach from design to service inspection is introduced using three new types of statistical quantities: (1) “system reliability index,” or “system co-reliability target” for any system consisting of structures and components, (2) “structural co-reliability,” for any structure, and (3) “component co-reliability” for any component. In a recent paper published in the International Journal of Pressure Vessels and Piping, Fong, Heckert, Filliben, and Freiman [6] developed a new theory of fatigue and creep rupture life modeling for metal alloys at room and elevated temperatures such that the co-reliability of an uncracked component can be estimated from fatigue and creep rupture test data with simple loading histories. In this paper, we extend the theory to include a methodology to estimate the co-reliability of a cracked pipe from fatigue crack growth rate test data, probability of detection (POD) data, and nondestructive evaluation (NDE) of initial crack sizing data for simple loading histories. To illustrate an application of this new modeling approach, we present four numerical example case studies using (a) the fatigue failure data of six AISI 4340 steel specimens at room temperature (Dowling, N. E., 1973) for an uncracked steel pipe, and (b) the fatigue crack growth rate data of 17 specimens of 2024-T3 aluminum (von Euw, Hertzberg, and Roberts, 1972) for a cracked aluminum pipe (see discussion after Eq. (16)). The four cases are: (1) Uncracked and uninspected pipe. (2) Inspected pipe with a crack-found-location-and-size call. (3) Uncracked and inspected pipe with a no-crack-found call and a POD value. (4) Inspected pipe with a crack-found-location-and-size call and a structural health monitoring (SHM) program. Significance and limitations of this new fatigue life modeling approach to the estimation of component co-reliability of uncracked and cracked pipes are presented and discussed.