TY - CONF A1 - Heideklang, René A1 - Shokouhi, Parisa T1 - Application of Data Fusion in Nondestructive Testing (NDT) T2 - 16th International Conference on Information Fusion CY - Istanbul, Turkey DA - 2013-07-09 PY - 2013 AN - OPUS4-29780 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Heideklang, René A1 - Shokouhi, Parisa T1 - Data Fusion for Enhanced Flaw Detection T2 - 40th Annual Review of Progress in Quanitative Nondestructive Evaluation (QNDE) CY - Baltimore, MD, USA DA - 2013-07-21 PY - 2013 AN - OPUS4-29781 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Heideklang, René A1 - Shokouhi, Parisa T1 - Data Fusion for Enhanced Flaw Detection T2 - 40th Annual Review of Progress in Quantitative Nondestructive Evaluation (QNDE) CY - Baltimore, MD, USA DA - 2013-07-21 PY - 2013 AN - OPUS4-29782 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Heideklang, René A1 - Shokouhi, Parisa T1 - Application of data fusion in nondestructive testing (NDT) N2 - Applying contemporary data fusion techniques, the multi-modal nondestructive testing (NDT) data sets can be combined to obtain more reliable results. The reliability can be quantified in terms of the probability of detection of sought material defects. A concise review of the published studies on NDT data fusion is provided here and the key concepts and anticipated challenges are discussed. The detailed steps involved in the NDT fusion process are explained with reference to a case study. The presented data set includes the results of three different NDT techniques on a test specimen with built-in defects. Several pixel-level fusion algorithms were applied and their performances are quantitatively compared. T2 - FUSION 2013 - 16th International Conference on Information Fusion CY - Istanbul, Turkey DA - 2013-07-09 PY - 2013 SN - 978-605-86311-1-3 SP - 835 EP - 841 AN - OPUS4-30033 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Heideklang, René A1 - Shokouhi, Parisa T1 - Quantitative multi-modal NDT data analysis N2 - A single NDT technique is often not adequate to provide assessments about the integrity of test objects with the required coverage or accuracy. In such situations, it is often resorted to multi-modal testing, where complementary and overlapping information from different NDT techniques are combined for a more comprehensive evaluation. Multimodal material and defect characterization is an interesting task which involves several diverse fields of research, including signal and image processing, statistics and data mining. The fusion of different modalities may improve quantitative nondestructive evaluation by effectively exploiting the augmented set of multi-sensor information about the material. It is the redundant information in particular, whose quantification is expected to lead to increased reliability and robustness of the inspection results. There are different systematic approaches to data fusion, each with its specific advantages and drawbacks. In our contribution, these will be discussed in the context of nondestructive materials testing. A practical study adopting a high-level scheme for the fusion of Eddy Current, GMR and Thermography measurements on a reference metallic specimen with built-in grooves will be presented. Results show that fusion is able to outperform the best single sensor regarding detection specificity, while retaining the same level of sensitivity. T2 - Quantitative nondestructive Evaluation conference 2013 (QNDE) CY - Baltimore, MD, USA DA - 2013-07-21 KW - Data fusion KW - Multi-sensor KW - Reliability PY - 2013 SP - 1 EP - 5(?) AN - OPUS4-30058 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Heideklang, René A1 - Shokouhi, Parisa T1 - Gemeinsame Erkennung oberflächenoffener Risse auf der Basis von Multi-Sensor Datensätzen T2 - DGZfP-Jahrestagung 2014 CY - Potsdam DA - 2014-05-26 PY - 2014 AN - OPUS4-33475 LA - deu 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, Parisa T1 - Multi-sensor image fusion at signal level for improved near-surface crack detection N2 - This study aims at improving the detection of near-surface defects in magnetizable and conductive specimens by combining the measurements of eddy current, magnetic flux leakage and thermography testing. Different signal processing methods for data normalization are proposed to enable data fusion at the pixel level. These methods are applied to a test specimen which contains 10 variably-sized defects. We quantitatively evaluate the performances of a total of 29 detection methods with respect to false alarm reduction at a fixed level of true positive rate. We report that false positive rate could be reduced from 1.65% down to 0.28% by the best multi-sensor method compared to the best single-sensor performance on the smallest defect, when 50% found flaw pixels are required for successful detection. KW - Data fusion KW - Image processing KW - Surface flaw KW - Detection PY - 2015 DO - https://doi.org/10.1016/j.ndteint.2014.12.008 SN - 0963-8695 VL - 71 SP - 16 EP - 22 PB - Elsevier Ltd. AN - OPUS4-32577 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 - THES A1 - Heideklang, René T1 - Data fusion for multi-sensor nondestructive detection of surface cracks in ferromagnetic materials N2 - Fatigue cracking is a dangerous and cost-intensive phenomenon that requires early detection. But at high test sensitivity, the abundance of false indications limits the reliability of conventional materials testing. This thesis exploits the diversity of physical principles that different nondestructive surface inspection methods offer, by applying data fusion techniques to increase the reliability of defect detection. The first main contribution are novel approaches for the fusion of NDT images. These surface scans are obtained from state-of-the-art inspection procedures in Eddy Current Testing, Thermal Testing and Magnetic Flux Leakage Testing. The implemented image fusion strategy demonstrates that simple algebraic fusion rules are sufficient for high performance, given adequate signal normalization. Data fusion reduces the rate of false positives is reduced by a factor of six over the best individual sensor at a 10 μm deep groove. Moreover, the utility of state-of-the-art image representations, like the Shearlet domain, are explored. However, the theoretical advantages of such directional transforms are not attained in practice with the given data. Nevertheless, the benefit of fusion over single-sensor inspection is confirmed a second time. Furthermore, this work proposes novel techniques for fusion at a high level of signal abstraction. A kernel-based approach is introduced to integrate spatially scattered detection hypotheses. This method explicitly deals with registration errors that are unavoidable in practice. Surface discontinuities as shallow as 30 μm are reliably found by fusion, whereas the best individual sensor requires depths of 40–50 μm for successful detection. The experiment is replicated on a similar second test specimen. Practical guidelines are given at the end of the thesis, and the need for a data sharing initiative is stressed to promote future research on this topic. KW - Data fusion KW - Non destructive testing KW - Multi-sensor KW - Surface cracks PY - 2018 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-471339 DO - https://doi.org/10.18452/19586 SP - 1 EP - 157 CY - Berlin AN - OPUS4-47133 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -