Best practices for evaluating the capability of nondestructive evaluation (NDE) and structural health monitoring (SHM) techniques for damage characterization

Aldrin, John C.; Annis, Charles; Sabbagh, Harold A.; Lindgren, Eric A.

doi:10.1063/1.4940646

Cited by 18 publications

(16 citation statements)

References 8 publications

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“…Because of the nature of SHM, it is difficult to conduct economical POD studies like those suggested in the main part of MIL-HDBK-1823A (2009) (e.g., it is suggested there that 40 or more craxcks shouold be used and for an SHM-POD study sensor/crack combinations where cracks will be grown over time) for traditional NDE. Aldrin et al (2016) outline general considerations that will be needed to provide a valid characterization of SHM capability.…”

Section: Background and Related Literaturementioning

confidence: 99%

Statistical Methods for Probability of Detection in Structural Health Monitoring

Meeker¹,

Roach²

2019

Structural Health Monitoring 2019

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There is much interest in the potential to use Structural Health Monitoring (SHM) technology to augment traditional Nondestructive Evaluation (NDE) methods to improve safety, increase asset availability, and reduce maintenance and inspection costs. SHM has the potential to be used in many areas of application including critical components in aircraft and pipelines. Probability of detection (POD) plays a critical role in aircraft structural integrity programs. As such, there has been a high interest in developing methods that can be used to assess POD in SHM applications. In contrast to traditional NDE laboratory experiments to assess POD that involve a set of specimens with cracks, SHM sensors are fixed and SHM data are acquired over time as cracks grow or otherwise evolve. Traditional statistical methods for assessing POD (e.g., as described in MIL-HDBK 1823A 2009) have to be extended to properly handle repeated-measures data. This purpose of this paper is to review the basic statistical concepts of probability of detection (POD) and to show how these concepts can and should be applied to SHM POD studies by modifying and extending existing methods for estimating POD. The methods presented here are applicable when there is a scalar damage index or other response that will be used to make a detect decision. The paper compares a simple model based on length at detection and a random effects model to describe repeated measures data.

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Section: Background and Related Literaturementioning

confidence: 99%

Statistical Methods for Probability of Detection in Structural Health Monitoring

Meeker¹,

Roach²

2019

Structural Health Monitoring 2019

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“…Aging of the structure leads to adverse effects. In Aldrin et al, 13 Monte Carlo simulation of flaw size as a function of time is proposed. The results demonstrate the sensitivity of flaws to degradation of SHM system.…”

Section: Introductionmentioning

confidence: 99%

A novel feature-based probability of detection assessment and fusion approach for reliability evaluation of vibration-based diagnosis systems

Ameyaw

Rothe

Söffker

2019

Structural Health Monitoring

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Structural health monitoring systems are based on suitable sensor techniques allowing online and offline supervision of technical systems. The quantification of sensors/measurement devices is a key issue for qualifying their effectiveness and efficiency and therefore to ensure safe operations. Probability of detection serves as a performance measure for quantifying the reliability of conventional nondestructive testing procedures taking into account statistical variability of sensor-based measurements. For vibration-based supervision approaches and fault detection and isolation methods, the probability of detection approach cannot be applied similarly. This results mainly from the complexity of the dynamical behavior of systems monitored in relation to faults, sensors position (observability), and the related feature extraction or monitoring task. In this contribution, probability of detection evaluation of vibration-based fault detection of elastic mechanical structures to be monitored is developed. Beside a principal discussion of the problem serving as introduction, an example using different sensor types in combination with mechanical modifications of an elastic beam is presented. The a90/95-criteria representing probability of 90% detection at a confidence level of 95% is examined to the measurements and related outcomes. Based on the analysis of a suitably chosen feature (like eigenfrequency or band power) and dependent on the mechanical modes considered, the efficiency and deficiency of the different combinations are shown. Based on the proposed approach, a new insight into the usefulness of different sensor type and fault position combination becomes possible. To improve the detection quality, suitable assumptions in combination with sensor/information fusion are applied to feature-based analysis as detection task using vibration measurements. In addition, based on an experimental evaluation, it can be concluded that a suitable fault-feature probability of detection analysis can be successfully implemented as a new reliable measure for vibration-based fault detection and isolation approaches. Furthermore, decision fusion as the combination of different measurements will allow the improvement of results. Dependent on noise analysis, a trade-off between flaw size detection and probability of falsely characterizing a fault with a90/95 reliability level can be attained.

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“…17–19 Many works have been done to extend this concept to SHM. 19–37 Recently, Janapati et al 19 characterized the performance of a pitch-catch SHM system for detecting a crack on an aluminum plate. In this study, 30 identical coupons instrumented in the same way are considered, thus providing the statistical evolution of a damage index (DI) over several factors such as the size and the orientation of the damage, the variations of mechanical properties of the structure, and the adhesive layer below transducers.…”

Section: Introductionmentioning

confidence: 99%

“…This requires a large number of inspection tasks for characterizing various configurations of damage that can potentially appear in the structure [17,18,19]. Many works have been done to extend this concept to SHM [20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,19,37]. Recently, Janapati et al characterized the performance of a pitch-catch SHM system for detecting a crack on an aluminum plate [19].…”

Section: Introductionmentioning

confidence: 99%

“…The authors conclude that duplicating coupon tests is a costly, time consuming and thus, not adapted approach for quantifying SHM systems. Nevertheless, this limitation can be overtaken by using a model-assisted approach, which produces so called ModelAssisted Probability of Detection (MAPOD) curves [21,25,26,35]. This approach, more adapted to SHM, was applied for assessing performance of various GW-based SHM systems [22,29,36,37].…”

Section: Introductionmentioning

confidence: 99%

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A model-based approach for statistical assessment of detection and localization performance of guided wave–based imaging techniques

Moriot

Quaegebeur

Duff

et al. 2017

Structural Health Monitoring

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This paper aims at providing a framework for assessing the detection and localization performance of guided wave-based structural health monitoring (SHM) imaging systems. The assessment exploits a damage identification metric (DIM) providing a diagnostic of the structure from an image of the scatterers generated by the system, allowing detection, localization, and size estimation of the damage. Statistical probability of detection (POD) and probability of localization (POL) curves are produced based on values of the DIM for several damage sizes and positions. Instead of relying on arduous measurements on a significant number of structures instrumented in the same way, a model-based approach is considered in this paper for estimating POD and POL curves numerically. This approach is first illustrated on a simplistic model, which allows characterizing the robustness of the SHM system for various levels of noise in test signals. An experimental test case using a more realistic case with an artificial damage is then considered for validating the approach. A good agreement between experimental and numerical values of the DIM and derived POD and POL curves is observed.

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Best practices for evaluating the capability of nondestructive evaluation (NDE) and structural health monitoring (SHM) techniques for damage characterization

Cited by 18 publications

References 8 publications

Statistical Methods for Probability of Detection in Structural Health Monitoring

Statistical Methods for Probability of Detection in Structural Health Monitoring

A novel feature-based probability of detection assessment and fusion approach for reliability evaluation of vibration-based diagnosis systems

A model-based approach for statistical assessment of detection and localization performance of guided wave–based imaging techniques

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