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Today, it is an established fact that the capability of the non-destructive testing (NDT) to find flaws can be properly addressed only in terms of probability of detection (POD). The probabilistic, signal-response model, introduced in 1980s, was developed with experimental observation of eddy-current inspections of flat plate samples, containing surface breaking cracks. A linearity between the peak voltage measured by the testing system, and the crack depth was observed. The influence of the crack depth was therefore seen as the major influencing factor for the POD, whereas other factors merely caused the variability in the measurement. This model has proven itself valid for those inspection cases where there is only one main influencing factor on the POD (usually the flaw size) and other factors have a lesser influence. But with increasing requirements to quantify the capability of NDT systems in complex inspection situations, where several factors have a major influence on the POD, it has become clear that the applicability of this simple model has reached its limits. In disregard to its limitations, this model is regularly applied to those situations in which its fundamental assumptions are invalid, forcing evaluators and NDT researchers into attempts to fit the data to an unsuitable model, instead of fitting an appropriate model to the data.
The multiparameter POD model, developed in the early 2010s, enables more factors that influence detection to be simultaneously analysed, making the POD a function of multiple factors. The model is based on the inspection’s physical model, to describe the influence of different factors on the response signal. Measurement variability is obtained from the experiment. Using this model assisted determination of the POD, the necessary number of flaws for evaluation is reduced and a more comprehensive understanding of the inspection is obtained. Several examples of the successful application of the multi-parameter POD model in different fields will be presented.
The non-destructive testing methods available for civil engineering (NDT-CE) enable the measurements of quantitative parameters, which realistically describe the characteristics of existing buildings. In the past, methods for quality evaluation and concepts for validation expanded into NDT-CE to improve the objectivity of measured data. Thereby, a metrological foundation was developed to collect statistically sound and structurally relevant information about the inner construction of structures without destructive interventions. More recently, the demand for recalculations of structural safety was identified. This paper summarizes a basic research study on structural analyses of bridges in combination with NDT. The aim is to use measurement data of nondestructive testing methods as stochastic quantities in static calculations. Therefore, a methodical interface between the guide to the expression of uncertainty in measurement and probabilistic approximation procedures (e.g. FORM) has been proven to be suitable. The motivation is to relate the scientific approach of the structural analysis with real information coming from existing structures and not with those found in the literature. A case study about the probabilistic bending proof of a reinforced concrete bridge with statistically verified data from ultrasonic measurements shows that the measuring results fulfil the requirements concerning precision, trueness, objectivity and reliability.
Issues that prevent Structural Health Monitoring (SHM) based on Guided Waves (GW) from being a part of today’s monitoring solutions in industry are not all obvious to the scientific community. To uncover and overcome these issues, scientists working on SHM and GW problems joined in an expert committee under the patronage of the German Society for Non-Destructive Testing. An initiated online survey among more than 700 experts and users reveals the hurdles hindering the practical application of GWbased SHM. Firstly, methods for proof of reliability of SHM approaches are missing.
Secondly, detailed understanding of phenomenological described wave-damage interactions is needed. Additionally, there are significant unsolved implementation issues and unsolved problems of signal processing including handling of environmental influences.
To enable substantial proof of reliability without unaffordable experimental effort also efficient simulation tools including realistic damage interaction are needed, enabling the joint use of experimental and simulated data to predict the capabilities of the Monitoring system. Considering these issues, the committee focusses on simulation, signal processing, as well as probability of detection and standardization. In the presented work, recent activities of the expert committee starting with survey results are summarized. An open access data basis of life-like measurements is presented to allow testing and comparison of signal processing and simulation algorithms. Finally, a strategy for efficient proof of reliability increasing the acceptance of SHM in industry and for successful Integration of SHM into real-world engineering structures is proposed.
Non-destructive testing (NDT) is a major contributor to the safe railway operation. Even though NDT reliability in railway maintenance is affected by human factors, there are only just a few studies published in this field so far. Education and training of the NDT personnel are some of the most important drivers of safe and reliable NDT. Continuously improving current practices and tools used for educational purposes can be achieved not only through technical content, but also through the attention to human factors. The aim of this study was to deepen the understanding of possible human-related risks in the manual ultrasonic inspection of the hollow railway axles and to suggest measures to improve the education and training of the NDT personnel. This was achieved by means of Failure Modes and Effects Analysis (FMEA) carried out with eight NDT experts and by a survey of 27 experienced inspectors. The results show that failures can happen throughout the entire NDT process. Prevention of those failures could be improved through the optimization of the organization, technology, documentation and regulations, working conditions and the general process, and through the optimization of the formal education and training. Specialized training of the executives, extended training of the supervisors and the inspectors and improvement of the inspection documentation have been suggested. The study also showed potential for the improvements of the inspection in the field.
This presentation offers a holistic view on the assessment of reliability of NDT including the intrinsic reliability (typically expressed in terms of probability of detection (POD) curves), application, human and organisational factors. In addition to POD, advanced methods such as multiparameter POD, volume POD and POD for combined data are presented. Human and organisational factors in NDT require a systematic approach, i.e. it is not just the individual that determines how the inspections are carried out but also the interactions of individuals with the technology, team, organisation and the extra-organisational environment. Lessons learned from the literature as well as from own studies are presented.
That human factors (HF) affect the reliability of NDT is not novelty. Still, when it comes to reliability assessments, the role of people is often neglected. Reliability is typically expressed in terms of POD curves, and the effects of human and organisational factors on the inspection are typically tackled by the regulations, procedures and by the qualification and training of the inspection personnel. However, studies have shown that even the most experienced personnel can make mistakes and that the reliability in the field is never as high as the reliability measured in the POD experiments. Generally, HF are considered too unpredictable and too uncontrollable to model. If that is the fact, then what can we do? The engineering perspective to this problem has often been to find ways to automate inspections and, recently, to make use of artificial intelligence tools to decrease the direct effect of people on the inspection results and improve the overall efficiency and reliability. However, despite automation and AI, people remain the key players, though their tasks change. The contemporary approach to HF is not to engineer them out of the system but to design human-machine systems that make the best use of both. In this talk, ways of tackling HF in the design of systems and processes will be presented.
Introduction. Comparing different emitter classes and rationally designing the next generation of molecular and nanoscale probes for bioimaging applications require accurate and quantitative methods for the measurement of the key parameter photoluminescence quantum yield f.1 f equals the number of emitted per number of absorbed photons. This is particularly relevant for increasingly used fluorescence imaging in the short wave-infrared region (SWIR) ≥ 900 nm providing deeper penetration depths, a better image resolution, and an improved signal-to-noise or tumor-to-background ratio.2, 3 However, spectroscopic measurements in the SWIR are more challenging and require specific calibrations and standards.
In this contribution, an approach is outlined to process non-destructively gath-ered measurement data in a comparable way in order to include the measured information in probabilistic reliability assessments of existing structures. An es-sential part is the calculation of measurement uncertainties. The effect of incor-porating evaluated NDT-results is demonstrated by means of a prestressed con-crete bridge and GPR measurements conducted on this bridge as a case-study. The bridge is assessed regarding SLS Decompression using the NDT-results.
How much do we, the small-angle scatterers, influence the results of an investigation? What uncertainty do we add by our human diversity in thoughts and approaches, and is this significant compared to the uncertainty from the instrumental measurement factors?
After our previous Round Robin on data collection, we know that many laboratories can collect reasonably consistent small-angle scattering data on easy samples1. To investigate the next, human component, we compiled four existing datasets from globular (roughly spherical) scatterers, each exhibiting a common complication, and asked the participants to apply their usual methods and toolset to the quantification of the results https://lookingatnothing.com/index.php/archives/3274).
Accompanying the datasets was a modicum of accompanying information to help with the interpretation of the data, similar to what we normally receive from our collaborators. More than 30 participants reported back with volume fractions, mean sizes and size distribution widths of the particle populations in the samples, as well as information on their self-assessed level of experience and years in the field.
While the Round Robin is still underway (until the 25th of April, 2022), the initial results already show significant spread in the results. Some of these are due to the variety in interpretation of the meaning of the requested parameters, as well as simple human errors, both of which are easy to correct for. Nevertheless, even after correcting for these differences in understanding, a significant spread remains. This highlights an urgent challenge to our community: how can we better help ourselves and our colleagues obtain more reliable results, how could we take the human factor out of the equation, so to speak?
In this talk, we will introduce the four datasets, their origins and challenges. Hot off the press, we will summarize the anonymized, quantified results of the Data Analysis Round Robin. (Incidentally, we will also see if a correlation exists between experience and proximity of the result to the median). Lastly, potential avenues for improving our field will be offered based on the findings, ranging from low-effort yet somehow controversial improvements, to high-effort foundational considerations.