Probability Of Detection Model Research Articles

Bicharacteristic probability of detection of crack under multi-factor influences using alternating current field measurement technique

Alternating Current Field Measurement (ACFM) technique has been widely used in various fields such as oil & gas, aerospace areas. However, during the detection process, the probability of detection (POD) is influenced by multi-factors such as lift-off height, scanning angle and detection speed. In this paper, a bicharacteristic probability of detection (BPOD) model is developed for quantifying the crack detection reliability using integrated Bx and Bz signals in the ACFM technique. A multidimensional BPOD model is established to study the influence of multiple factors on the detection probability of cracks using ACFM technique. A Bayesian network-based method is proposed to reverse the hierarchical impact weights of influence factors on the BPOD of the ACFM technique. The results show that the BPOD model considering both Bx and Bz response signals can evaluate smaller defects than the conventional POD model. The multiple influencing factors can substantially decrease the BPOD of cracks. The hierarchical impact weights on BPOD for cracks are ranked as follows: lift-off height, detection speed, personnel involved in the detection process and scanning angle. This approach can retrospectively find the potential contributors to the missed detections based on the BPOD.

POD methodologies for SHM application in civil engineering

Structural Health Monitoring (SHM) technologies can significantly enhance safety through continuous monitoring. The successful deployment of real-time SHM systems rely mainly on reliability evaluations. While numerous standards exist for reliability assessments or Probability of Detection (POD) of NDT techniques, there are no specific standards guiding reliability procedures for SHM data. Consequently, NDT POD methods are often applied to SHM data, despite the time and statistically dependent nature of SHM data. Especially in the field of civil engineering, there is the need for a SHM specific POD approach able to perform a meaningful reliability evaluation. Hence, in the current work, different SHM POD models including random intercept model and length at detection (LaD) model are implemented for a vibration-based monitoring data relevant for civil engineering. Unlike the linear response observed in NDT inspection data concerning damage, SHM data can exhibit non-linear characteristics in certain cases. In those cases, an advanced LaD model suitable for non-linear data is also developed in the current study. In addition, a comparison between the results obtained using NDT POD models and SHM POD models is demonstrated using an open-source GUI (TU1402) based on FE model. Moreover, the results obtained by the application of modified LaD POD model to a real test bridge data is demonstrated. Keywords: SHM, reliability, POD, civil engineering.

Supplemental structured surveys and pre-existing detection models improve fine-scale density and population estimation with opportunistic community science data

Density and population estimates aid in conservation and stakeholder communication. While free and broadly available community science data can effectively inform species distribution models, they often lack the information necessary to estimate imperfect detection and area sampled, thus limiting their use in fine-scale density modeling. We used structured distance-sampling surveys to model detection probability and calculate survey-specific detection offsets in community science models. We estimated density and population for 16 songbird species under three frameworks: (1) a fixed framework that assumes perfect detection within a specified survey radius, (2) an independent framework that calculates offsets from an independent source, and (3) a calibration framework that calculates offsets from supplemental surveys. Within the calibration framework, we examined the effects of calibration dataset size and data pooling. Estimates of density and population size were consistently biased low in the fixed framework. The independent and calibration frameworks produced reliable estimates for some species, but biased estimates for others, indicating discrepancies in detection probability between structured and community science surveys. The calibration framework produced reliable population estimates with as few as 10 calibration surveys with positive detections. Data pooling dramatically decreased bias. This study provides conservationists and managers with a cost-effective method of estimating density and population.

Edge Response and Defect Detectability in Flat Panel Digital Radiography

Defect detectability studies are used in nondestructive testing to ascertain the reliability of the method of inspection. In digital radiography, with the growing prevalence of automation of quality control processes by image processing and machine learning, a threshold detection criterion based on quantifiable data from the digital radiograph could be explored. The use of the parameter contrast-to-noise ratio (CNR) of defect signal as a probability of detection (POD) threshold criterion is explored in this paper. A stainless steel block containing artificial defects of known dimensions and location is radiographed by a flat panel detector, and an empirical POD curve is constructed. Before the POD study, the edge response of the flat panel system is studied to ensure noninterference of adjacent defect signals, gain insights about the lateral spread of the defect signal, and provide information to choose the region of interest for CNR calculation. The effect of noise on the POD using CNR as the threshold criterion is also included in the present study. The use of CNR-based POD models for digital radiography to aid the comparison and development of automatic defect detection models is also discussed.

Evaluating differences in density estimation for central Iowa butterflies using two methodologies.

The Pollard-Yates transect is a widely used method for sampling butterflies. Data from these traditional transects are analyzed to produce density estimates, which are then used to make inferences about population status or trends. A key assumption of the Pollard-Yates transect is that detection probability is 1.0, or constant but unknown, out to a fixed distance (generally 2.5 m on either side of a transect line). However, species-specific estimates of detection probability would allow for sampling at farther distances, resulting in more detections of individuals. Our objectives were to (1) evaluate butterfly density estimates derived from Pollard-Yates line transects and distance sampling, (2) estimate how detection probabilities for butterflies vary across sampling distances and butterfly wing lengths, and (3) offer advice on future butterfly sampling techniques to estimate population density. We conducted Pollard-Yates transects and distance-sampling transects in central Iowa in 2014. For comparison to densities derived from Pollard-Yates transects, we used Program DISTANCE to model detection probability (p) and estimate density (D) for eight butterfly species representing a range of morphological characteristics. We found that detection probability among species varied beyond 2.5 m, with variation apparent even within 5 m of the line. Such variation correlated with wing size, where species with larger wing size generally had higher detection probabilities. Distance sampling estimated higher densities at the 5-m truncation for five of the eight species tested. At this truncation, detection probability was <0.8 for all species, and ranged from 0.53 to 0.79. With the exception of the little yellow (Pyrisitia lisa), species with median wing length <5.0 mm had the lowest detection probabilities. We recommend that researchers integrate distance sampling into butterfly sampling and monitoring, particularly for studies utilizing survey transects >5 m wide and when smaller species are targeted.

Integration of multiple wall thinning parameters into the probability of detection analysis for long-range pipe inspection using microwaves

A three-dimensional probability of detection (POD) model integrating the depth, circumferential angle, and location of pipe wall thinning (PWT) was proposed for the probabilistic analysis of long-range pipe inspection using microwaves. Microwaves in TM01 mode were used to detect PWT with different sizes and locations (up to 13.5 m). The microwave signals reflected from the PWT were measured and processed, and their amplitudes were extracted and treated as the signal response. The results showed that the signal response was rarely influenced by the axial length of the PWT, while its natural logarithm was proportional to the natural logarithm of the PWT depth and circumferential angle. In addition, the signal response decreased exponentially with the PWT location. Subsequently, a multivariate regression model was established, and the related coefficients were estimated using the maximum likelihood method. The POD calculated from the regression model suggests that a small PWT at a far distance (e.g., 36.0 m) can be reliably detected using microwaves. Compared with a two-dimensional POD model that considers only the PWT cross-section area as the size parameter, the proposed POD model clearly shows that the POD is more dependent on the circumferential angle than on the depth of the PWT.

Simulation-Based Investigation of a Probability of Detection (POD) Model Using Phased Array Ultrasonic Testing (PAUT) Technique

Probability of detection (POD) as a metric for quantifying the capability of inspection procedures in nondestructive evaluation (NDE), has been applied and evolved in industries since 1970s. Progress had been noted when certain statistical functions were brought up to model POD behavior, including log-normal model (also referred as Probit model). This model had been concluded to be the best fit and therefore has been widely used in many studies, while the involved assumptions and conditions were not carefully addressed and explored. To make flexible POD datasets available for specific inspection procedures and reduce the number of expensive experiments needed, model-assisted POD (MAPOD) is an alternative. This paper addresses a pure simulation-based POD procedure of an inspection scenario involving phased array ultrasonic testing (PAUT) on lack-of-fusion defects in additive manufactured (AM) components. The mathematical simulations are performed by an ultrasonic testing (UT) simulation software, simSUNDT, developed at Chalmers University of Technology in Sweden. Resulted inspection datasets with the proposed data processing steps are evaluated in terms of the assumptions and conditions of log-normal POD model, with the purpose of discussing the POD model validity under different circumstances. Simulation-based POD curves are finally compared with several discrete POD values at some defect sizes, calculated through massive computations from physics-model based metamodel. Comparisons and observations confirm satisfactory application of log-normal POD model despite some violations in model hypotheses.

Performance assessment of multi-MFL inspection using feature-based POD

Oil and gas pipelines, which transport large quantities of oil products and natural gas, are subject to pipeline failures caused by corrosion. Magnetic flux leakage (MFL) is one of the most popular non-destructive testing (NDT) techniques for the detection of pipeline corrosion. Since individual MFL is insensitive to the corrosion components that are parallel with its magnetic field, two types of MFL tools with perpendicular magnetic fields are usually employed in one inspection to detect all corrosion defects. This study applies probability of detection (POD) to quantitatively assess the detection capabilities of two individual MFL tools and their combination. Due to the characteristics of MFL inspection, this study proposes the construction of the POD model as a function of two geometric features, namely the volume and the orientation, which have a significant influence on the MFL signal response. Detection results from two MFL tools are integrated using logical OR operation to study the POD of their combination. With the proposed POD model, the minimum criteria that ensure a corrosion defect will be reliably detected by MFL tools are studied in this paper. The validity of the proposed POD model is justified on the data collected from an in-service pipeline.

Multivariate probability of detection (POD) analysis considering the defect location for long-range, non-destructive pipe inspection using electromagnetic guided wave testing

This paper presents a multivariate probability of detection (POD) analysis method for long-range pipe inspection using microwave non-destructive testing (NDT). The proposed two-dimensional (2D) POD model considers both the size and location of pipe wall thinning (PWT) and is thus able to simultaneously characterize the detectability and detection range. First, the propagation of microwaves in a pipe with PWT was modeled through numerical simulation. The simulated S-parameters (S11) were converted into the time domain and were further processed to correlate the amplitude of the reflection signal with the corresponding PWT depth and location. The simulation results indicate that the logarithmic amplitude of the reflection signal is proportional to the PWT location and logarithmic PWT depth. Therefore, a prior multivariate regression model was established for the above three parameters. Second, an experiment was performed using a 14.5-m-long brass pipe. Twelve short pipes with six different inner diameters were used to emulate PWT and were deployed at eight different locations in the pipe. The experimental results suggest that each PWT can be detected with excellent repeatability, and show consistency with the simulation results. The processed experimental signals were subsequently employed for the regression model of the amplitude of the reflection signal, PWT depth, and location. Finally, a 2D POD model was constructed on the basis of the regression model and a dynamic-static threshold, combining in-pipe microwave signal attenuation with instrumental noises. The POD plot and contour give reasonable interpretations of the detection capability of this testing method against PWTs with different depths, considering the PWT locations.

Validation of the Clear Safety Listeria Method for Detection of Listeria Species in Hot Dogs and on Environmental Surface Matrixes: AOAC Performance Tested MethodSM 091901.

The Clear Safety Listeria method utilizes polymerase chain reaction (PCR) amplification and targeted next-generation sequencing technology to detect Listeria species (L. monocytogenes, L. innocua, L. ivanovii, L. marthii, L. grayi, L. welshimeri, and L. seeligeri) in hot dogs and on selected environmental surfaces. The aim was to validate the candidate method according to current AOAC guidelines. The candidate method was compared to the reference method for hot dogs and the environmental surfaces. The method was also evaluated for inclusivity and exclusivity using 50 inclusivity strains and 30 exclusivity strains for each reported target. Product consistency and stability was tested and robustness was evaluated with changes in enrichment temperature, volume of sample treatment, and aliquot volume for PCR. The candidate method demonstrated no statistically significant differences using the probability of detection model between candidate and reference methods or between presumptive and confirmed results for all environmental surfaces and hot dogs. Additionally, the candidate method detected all inclusivity organisms and excluded all exclusivity organisms for each reported target. Product lots were shown to be consistent and data supported the kit's shelf life. Finally, the robustness study demonstrated no statistical differences when the volume of sample or the aliquot volume for PCR was altered. Increasing the incubation temperature to 37 ± 1 °C resulted in greater recovery of L. monocytogenes as compared to 35 ± 1 °C and 30 ± 1 °C. The Clear Safety Listeria method is statistically equivalent to the reference methods for the detection of L. monocytogenes and Listeria spp. in hot dogs and on selected environmental surfaces. The Clear Safety Listeria method is an automated, highthroughput NGS-based method capable of detecting Listeria species in the hot dog and environmental samples within 28h.

Estimating Sightability for Helicopter Surveys Using Surrogates of White‐Tailed Deer

ABSTRACTOn large management units where terrain allows observation of white‐tailed deer (Odocoileus virginianus) from the air, helicopter surveys may provide managers with cost‐effective and accurate estimates of population abundance; however, imperfect detection of deer introduces negative bias. This can result in potentially inappropriate management recommendations (e.g., harvest prescriptions). Sightability estimators are designed to model detection heterogeneity based on factors that affect observer detection of target animals. Sightability models are widely used to estimate ungulate abundance; however, previous researchers have developed sightability models using radio‐collared animals, potentially introducing bias during model development attributed to unaccounted for movement between initial and follow‐up flights. Therefore, our objectives were to model detection probability using surrogates of white‐tailed deer as a function of covariates including distance from transect, vegetative obstruction, and light conditions to improve population estimates derived from helicopter surveys and apply our final sightability model in operational helicopter surveys. We conducted our study in 2019 on a 399.4‐km2 study area within a 1,210‐km2 cattle ranch in central Florida, USA. We placed 3‐dimensional archery targets as surrogates for white‐tailed deer at assigned locations unknown to observers across a combination of stratified bins of vegetative obstruction and distances from the transect. Our top model indicated that distance from the transect and vegetative obstruction negatively affected detection of deer. Detection probability on the flight transect ranged from 0.95 (95% CI = 0.89–0.98) for vegetative obstruction 0–25% and distance from transect 0–25 m to 0.05 (95% CI = 0.01–0.18) for vegetative obstruction 76–100% and distance from transect 100–125 m. We applied our sightability model to operational surveys on 3 units, which produced population estimates averaging 26% higher than those derived from uncorrected counts. Observers simultaneously recorded live deer during operational flights, of which 60% of deer groups were observed while stationary. Our methods using stationary surrogates to develop region‐specific sightability models could be used in other study areas to account for imperfect detection of white‐tailed deer. © 2021 The Wildlife Society.

Evaluation of detection capability of eddy current probes with stochastic decision threshold for inspecting pits on austenitic stainless steel welding

It is still a big challenge to calculate the probability of defect detection for inspecting pits on austenitic stainless steel welding using traditional POD models. Because we determine the decision threshold, the POD curve has a lot of changes as the decision threshold changes, there is no clear reason to insist which threshold is reasonable. This study proposes a new probability of detection (POD) model to quantitatively assess the detection capability of eddy current probes for inspecting pits on austenitic stainless steel welding. The experimental results show that the proposed model is more reasonable than traditional ones. The novel POD model was employed to analyze three eddy current probes, uniform, TR, plus-point probes on austenitic stainless steel welding. The results reveal that the uniform eddy current probe has the best detection capability for inspecting pits on among the three probes.

Probabilistic evaluation of detection capability of eddy current testing to inspect pitting on a stainless steel clad using multiple signal features

This study proposes a probability of detection (POD) model to quantitatively evaluate the capability of eddy current testing to detect flaws on the inner surface of pressure vessels cladded by stainless steel and in the presence of high noise level. Welded plate samples with drill holes were prepared to simulate corrosion that typically appears on the inner surface of large-scale pressure vessels. The signals generated by the drill holes and the noise caused by the weld were examined using eddy current testing. A hit/miss-based POD model with multiple flaw parameters and multiple signal features was proposed to analyze the measured signals. It is shown that the proposed model is able to more reasonably characterize the detectability of eddy current signals compared to conventional models that consider a single signal feature.

Probabilistic evaluation of the area of coverage of a probe used for eddy current non-destructive inspections

This study proposes a method to probabilistically evaluate the area of coverage of nondestructive inspections to detect defects on a surface of a structure. For the specific problem, this study considers the effect of the distance between two neighboring scanning lines on the detectability of eddy current testing against near-side cracks. Thirty-eight type 316L stainless steel plates having a fatigue crack were prepared, and eddy current examinations were performed with a sufficiently fine scanning pitch. The full width at half maximum of the spatial distribution of the amplitude of the signals was approximated using a Gaussian function. A probability of detection model considering the distance between two neighboring scanning lines is proposed because in actual inspections a scanning line does not always run directly above a crack. The results demonstrated that the proposed model enables a reasonable probabilistic evaluation of the effect of the distance between two neighboring scanning lines.

Evaluating the use of thermal imaging cameras to monitor the endangered greater bilby at Astrebla Downs National Park

Spotlight surveys are widely used to monitor arid-zone-dwelling species such as the greater bilby (Macrotis lagotis). These surveys require a sufficient sample size to adequately model detection probability. Adequate sample sizes can be difficult to obtain for low-density populations and for species that avoid light and or have poor eyeshine like the bilby. Abundance estimates based on burrow counts can be problematic because of the variable relationship between the number of burrows used and bilby abundance. In 2013, feral predators devastated a Queensland bilby population and a method was required that could locate and monitor the remaining bilbies. We report on a study that compared density estimates derived from spotlighting and thermal cameras. Bilbies were surveyed annually over three years, using spotlights and thermal cameras on different nights but using the same transects to compare the methods. On average, thermal cameras detected twice the number of bilbies per kilometre surveyed than spotlighting. Despite this difference in the number of bilbies detected, density estimates (bilbies km−2) were similar (thermal camera versus spotlight: 0.6 versus 0.2 (2014), 3.4 versus 3.4 (2015) and 4.8 versus 3.3 (2016)). Nevertheless, the larger sample size obtained using thermal cameras gave greater confidence in modelling detection probability.

Probabilistic analysis of electromagnetic acoustic resonance signals for the detection of pipe wall thinning

ABSTRACT This study proposes a probability of detection (POD) model for the probabilistic analysis of the detectability of electromagnetic acoustic resonance (EMAR) method for the detection and evaluation of pipe wall thinning. Forty-one carbon steel plate samples with an artificially corroded groove were prepared to simulate pipe wall thinning caused by flow-assisted corrosion. Experiments were performed to gather EMAR signals from the samples, and subsequently the depths of the grooves were evaluated based on the fundamental frequency of the measured signals. The results of the experiments showed that the error in evaluating the depth of a groove tended to increase with the depth. The results also confirmed that the surface roughness of the groove would contribute to the error, and the thickness of a plate without corrosion can be quite accurately evaluated. Analysing the measured EMAR signals using the proposed POD model, which takes these characteristics into consideration, and a conventional one confirmed that the proposed model can more reasonably evaluate the probability of detection against small wall thinning, as well as the false-positive rate.

Matrix Extension Study: Listeria Right Now™ Test for Detection of Listeria spp. from Selected Environmental Surfaces Without Enrichment: AOAC Performance Tested MethodSM 081802.

Listeria Right Now™ is a novel, enrichment-free test for the detection of Listeria spp. in swab samples taken from environmental surfaces. Results are available in less than 1 h. In a previous Performance Tested MethodSM (PTM) study, the test was validated for swab samples from stainless-steel and sealed concrete surfaces. A PTM matrix extension study was conducted to validate the method for the detection of Listeria spp. in swab samples from ceramic tile, plastic, and rubber surfaces. Performance of the Listeria Right Now method was compared to that of the U.S. Food and Drug Administration Bacteriological Analytical Manual reference culture procedure for the detection of Listeria spp. in swab samples taken from inoculated ceramic tile, plastic, and rubber surfaces. Data were analyzed using a probability of detection model. There were no significant differences in performance between the Listeria Right Now and reference culture methods for any of the three surfaces tested, as determined by probability of detection analysis. The Listeria Right Now method is an effective procedure for the detection of Listeria spp. from a variety of environmental surfaces. Listeria Right Now provides accurate results, without enrichment, in real time. This enables food industry personnel to react swiftly to suspected Listeria contamination incidents.

Target characteristics and correlation detection probability calculation method in photoelectric detection screen testing system

AbstractThe changed environment factors will affect the capture rate of target and the detection performance in photoelectric detection screen testing system, to improve the detection performance of system, it is very necessary to establish the target optical characteristic model and correlation detection probability calculation model. Based on the detection principle of unit photoelectric detection screen and the spatial geometric relation of four photoelectric detection screens testing system, this article establishes a new target radiation characteristics calculation model by using target's radiation luminance and radiation energy, gives the output signal expression function, derived the detection probability function by signal to noise ratio (SNR) and false alarm probability. Through calculation analysis, the results show that the detection probability of four photoelectric detection screens testing system is related to the target optical characteristic, the output photoelectric current and SNR of photoelectric detection receiving sensor can describe target detection performance. According to the experiments testing, the proposed calculation model can effectively evaluate the performance of photoelectric detection screen testing system.

Matrix Extension Study: Listeria Right Now™ Test for Detection of Listeria spp. from Selected Environmental Surfaces Without Enrichment

Background: Listeria Right Now™ is a novel, enrichment-free test for the detection of Listeria spp. in swab samples taken from environmental surfaces. Results are available in less than 1 h. In a previous Performance Tested MethodSM (PTM) study, the test was validated for swab samples from stainless-steel and sealed concrete surfaces. Objective: A PTM matrix extension study was conducted to validate the method for the detection of Listeria spp. in swab samples from ceramic tile, plastic, and rubber surfaces. Methods: Performance of the Listeria Right Now method was compared to that of the U.S. Food and Drug Administration Bacteriological Analytical Manual reference culture procedure for the detection of Listeria spp. in swab samples taken from inoculated ceramic tile, plastic, and rubber surfaces. Data were analyzed using a probability of detection model. Results: There were no significant differences in performance between the Listeria Right Now and reference culture methods for any of the three surfaces tested, as determined by probability of detection analysis. Conclusions: The Listeria Right Now method is an effective procedure for the detection of Listeria spp. from a variety of environmental surfaces. Highlights: Listeria Right Now provides accurate results, without enrichment, in real time. This enables food industry personnel to react swiftly to suspected Listeria contamination incidents.

Fault diagnosis using Probability of Detection (POD)‐based sensor/information fusion for vibration‐based analysis of elastic structures

AbstractThe Probability‐of‐Detection (POD) measure quantifies the performance of a measurement device to nondestructive testing taking into account statistical variability. The time and cost involved have given rise to Model‐Assisted POD to improve the effectiveness of POD models with little or no specimen testing. Similar criteria regarding capabilities of FDI (Fault Detection and Isolation) or diagnostic approaches applied to vibration‐based analysis are currently not known. This results from complexity of the dynamical behaviors in relation to faults, sensor positions (observability), and feature extraction tasks. In this contribution the complexity of POD‐based analysis applied to fault detection of elastic mechanical structures is examined. Based on experimental results of mechanical modifications on an elastic beam, the POD of different FDI‐procedures are examined. Different sensor types are used for eigenfrequency analysis. Depending on the modes considered it can be shown why this classical approach often does not lead to satisfying results. To fuse the POD values, suitable assumptions in combination with a new method are applied to eigenfrequency‐based analysis as detection task.