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

Moriot, Jérémy; Quaegebeur, Nicolas; Duff, Alain Le; Masson, Patrice

doi:10.1177/1475921717744679

Cited by 43 publications

(35 citation statements)

References 52 publications

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“…However, the weighting of the evaluation has to be done carefully and requires information on the sensor network's condition (self diagnosis) and statistical data on the reliability of the given SHM results. Although various studies addressed the statistical reliability of different SHM methods, the generalization of results remains challenging [24,78,79]. However, appropriate statistical data should contain the reliability of a conclusion (e.g., the probability that true damage location is close to the predicted damage location, as shown in Figure 6b) [24].…”

Section: Definition Of Data Evaluation Proceduresmentioning

confidence: 99%

Review of Structural Health Monitoring Methods Regarding a Multi-Sensor Approach for Damage Assessment of Metal and Composite Structures

Kralovec

Schagerl

2020

Sensors

126

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Structural health monitoring (SHM) is the continuous on-board monitoring of a structure’s condition during operation by integrated systems of sensors. SHM is believed to have the potential to increase the safety of the structure while reducing its deadweight and downtime. Numerous SHM methods exist that allow the observation and assessment of different damages of different kinds of structures. Recently data fusion on different levels has been getting attention for joint damage evaluation by different SHM methods to achieve increased assessment accuracy and reliability. However, little attention is given to the question of which SHM methods are promising to combine. The current article addresses this issue by demonstrating the theoretical capabilities of a number of prominent SHM methods by comparing their fundamental physical models to the actual effects of damage on metal and composite structures. Furthermore, an overview of the state-of-the-art damage assessment concepts for different levels of SHM is given. As a result, dynamic SHM methods using ultrasonic waves and vibrations appear to be very powerful but suffer from their sensitivity to environmental influences. Combining such dynamic methods with static strain-based or conductivity-based methods and with additional sensors for environmental entities might yield a robust multi-sensor SHM approach. For demonstration, a potent system of sensors is defined and a possible joint data evaluation scheme for a multi-sensor SHM approach is presented.

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Section: Definition Of Data Evaluation Proceduresmentioning

confidence: 99%

Review of Structural Health Monitoring Methods Regarding a Multi-Sensor Approach for Damage Assessment of Metal and Composite Structures

Kralovec

Schagerl

2020

Sensors

126

View full text Add to dashboard Cite

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“…The damage-sensitive features from each individual sensor pair are used to create an damage index for that sensor pair. As the mean and standard deviation of the damage-sensitive features in pristine state are known from equations (13) and (14), the upper bound of the feature value from sensor pair i in pristine state can be calculated and serves as the damage detection threshold T h i for this sensor pair:…”

Section: Level 1: Quantification Of Noise Under Environmental and Opementioning

confidence: 99%

“…where R k is a Gaussian random variable with meanμ k and standard deviationσ k ,μ k andσ 2 k are calculated from equations (13) and (14). As the signals recorded via different sensor pairs are independent, I j is normally distributed with meanμ j and varianceσ 2 j ,…”

Section: Level 1: Quantification Of Noise Under Environmental and Opementioning

confidence: 99%

Hierarchical approach for uncertainty quantification and reliability assessment of guided wave-based structural health monitoring

Yue

Aliabadi

2020

Structural Health Monitoring

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In this article, a hierarchical approach is proposed for the design and assessment of a guided wave-based structural health monitoring system for the detection and localisation of barely visible impact damage in composite airframe structures. The hierarchical approach provides a systemic and practical way to establish guided wave-based structural health monitoring systems for different structures in the presence of uncertainties and to quantify system performance. The proposed approach is carried out in four steps: (1) determine optimal sensor placement for the target structure and its plausible impact scenarios, (2) set detection threshold for global damage index based on the noise level present in the required environmental and operations conditions, (3) detect damage in critical locations and quantify detection performance by calculating the probability of detection, probability of false alarm and detection accuracy and (4) locate the detected damage while also quantifying the accuracy of location estimation and the probability of correctly indicating if the damage is in an area critical to the integrity of the structure. The proposed approach is demonstrated in aircraft carbon fibre-reinforced polymer structures from coupon level (simple flat panels) to sub-component level (large flat panel with multiple carbon fibre-reinforced polymer stringers and aluminium frames) for the detection and localisation of barely visible impact damage.

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“…The United States Air Force (USAF), National Aeronautics and Space Administration (NASA), as well as many authors consider MIL-HDBK-1823A (updated version of MIL-HDBK-1823) as the state-of-the-art and contemporary guide for POD studies. 22,23 This article adapts the method and utilizes extracted features as response. It is worth mentioning that this research focuses on fault detection.…”

Section: Podmentioning

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

Cited by 43 publications

References 52 publications

Review of Structural Health Monitoring Methods Regarding a Multi-Sensor Approach for Damage Assessment of Metal and Composite Structures

Review of Structural Health Monitoring Methods Regarding a Multi-Sensor Approach for Damage Assessment of Metal and Composite Structures

Hierarchical approach for uncertainty quantification and reliability assessment of guided wave-based structural health monitoring

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

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