TY - JOUR A1 - Völker, Christoph A1 - Shokouhi, P. T1 - Clustering based multi sensor data fusion for honeycomb detection in concrete KW - Data fusion KW - Concrete evaluation KW - Honeycombing KW - Machine learning KW - Clustering KW - Density based clustering PY - 2015 DO - https://doi.org/10.1007/s10921-015-0307-7 SN - 0195-9298 SN - 1573-4862 VL - 34 IS - Article 32 SP - 1 EP - 10 PB - Plenum Press CY - New York, NY AN - OPUS4-35073 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Völker, Christoph A1 - Shokouhi, P. T1 - Multi sensor data fusion approach for automatic honeycomb detection in concrete PY - 2015 DO - https://doi.org/10.1016/j.ndteint.2015.01.003 SN - 0963-8695 VL - 71 SP - 54 EP - 60 PB - Butterworth-Heinemann CY - Oxford AN - OPUS4-35074 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Völker, Christoph A1 - Shokouhi, P. T1 - Data aggregation for improved honeycomb detection in concrete using machine learning-based algorithms N2 - We present the results of several machine learning (ML)- inspired data fusion algorithms applied to multi-sensory nondestructive testing (NDT) data. Our dataset consists of Impact-Echo (IE), Ultrasonic Pulse Echo (US) and Ground Penetrating Radar (GPR) data collected on large-scale concrete specimens with built–in simulated honeycombing defects. The main objective is to improve the detectability of honeycombs by fusing the information from the three different sensors. We describe normalization, feature detection and optimal feature selection. We have used unsupervised and supervised ML, i.e., classification and clustering, for data fusion. We demonstrate the advantage of data fusion in reducing the false positives up to 10% compared to the best single sensor, thus, improving the detectability of the defects. The methods were evaluated on a concrete specimen. The effectiveness of the proposed approach was demonstrated on a separate full-scale concrete specimen. The results indicate the transportability of the conclusions from one specimen to the other. T2 - NDT-CE 2015 - International symposium non-destructive testing in civil engineering CY - Berlin, Germany DA - 15.09.2015 KW - Data fusion KW - Concrete evaluation KW - Honeycombing KW - Machine learning KW - Clustering PY - 2015 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-350968 UR - https://www.ndt.net/?id=18364 SN - 1435-4934 VL - 20 IS - 11 SP - 1 EP - 8 PB - NDT.net CY - Kirchwald AN - OPUS4-35096 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Shokouhi, P. A1 - Heideklang, Rene T1 - Fusion of multi-sensory NDT data for reliable detection of surface cracks: Signal-level vs. decision-level N2 - We present and compare two different approaches for NDT multi-sensor data fusion at signal (low) and decision (high) levels. Signal-level fusion is achieved by applying simple algebraic rules to strategically post-processed images. This is done in the original domain or in the domain of a suitable signal transform. The importance of signal normalization for low-level fusion applications is emphasized in regard to heterogeneous NDT data sets. For fusion at decision level, we develop a procedure based on assembling joint kernel density estimation (KDE). The procedure involves calculating KDEs for individual sensor detections and aggregating them by applying certain combination rules. The underlying idea is that if the detections from more than one sensor fall spatially close to one another, they are likely to result from the presence of a defect. On the other hand, single-senor detections are more likely to be structural noise or false alarm indications. To this end, we design the KDE combination rules such that it prevents single-sensor domination and allows data-driven scaling to account for the influence of individual sensors. We apply both fusion rules to a three-sensor dataset consisting in ET, MFL/GMR and TT data collected on a specimen with built-in surface discontinuities. The performance of the fusion rules in defect detection is quantitatively evaluated and compared against those of the individual sensors. Both classes of data fusion rules result in a fused image of fewer false alarms and thus improved defect detection. Finally, we discuss the advantages and disadvantages of low-level and high-level NDT data fusion with reference to our experimental results. T2 - 42nd Annual Review of Progress in Quantitative Nondestructive Evaluation (QNDE) CY - Minneapolis, USA DA - 26.07.2015 KW - multi-sensory KW - NDT KW - signal-level KW - decision-level PY - 2016 SN - 978-0-7354-1353-5 DO - https://doi.org/10.1063/1.4940634 SN - 0094-243X VL - 1706 SP - Article Number: 180004 PB - AMER INST PHYSICS CY - Melville, USA AN - OPUS4-35870 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Heideklang, René A1 - Shokouhi, P. T1 - Decision-level fusion of spatially scattered multi-modal data for nondestructive inspection of surface defects N2 - This article focuses on the fusion of flaw indications from multi-sensor nondestructive materials testing. Because each testing method makes use of a different physical principle, a multi-method approach has the potential of effectively differentiating actual defect indications from the many false alarms, thus enhancing detection reliability. In this study, we propose a new technique for aggregating scattered two- or three-dimensional sensory data. Using a density-based approach, the proposed method explicitly addresses localization uncertainties such as registration errors. This feature marks one of the major of advantages of this approach over pixel-based image fusion techniques. We provide guidelines on how to set all the key parameters and demonstrate the technique's robustness. Finally, we apply our fusion approach to experimental data and demonstrate its capability to locate small defects by substantially reducing false alarms under conditions where no single-sensor method is adequate. KW - Multi-sensor data fusion KW - Density estimation KW - Scattered data KW - Defect detection KW - Nondestructive testing KW - Registration errors PY - 2016 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-352977 DO - https://doi.org/10.3390/s16010105 SN - 1424-8220 VL - 16 SP - Article Number: 105 PB - MDPI CY - Basel, Switzerland AN - OPUS4-35297 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Shokouhi, P. A1 - Heideklang, René T1 - Fusion of multi-sensory NDT data for reliable detection of surface cracks: Signal-level vs. decision-level T2 - 42nd Review of Progress in Quantitative Nondestructive Evlauation (QNDE) CY - Minneapolis, USA DA - 2015-07-25 PY - 2015 AN - OPUS4-33961 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -