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The combination of different types of sensors to multi-sensor devices offers excellent potential for monitoring applications. This should be demonstrated by means of four different examples of actual developments carried out by Federal Institute for Materials Research and Testing (BAM): monitoring and indoor localization of relief forces, a micro-drone for gas measurement in hazardous scenarios, sensor-enabled radio-frequency identification (RFID) tags for safeguard of dangerous goods, and a multifunctional sensor for spatially resolved under-surface monitoring of gas storage areas. Objective of the presented projects is to increase the personal and technical safety in hazardous scenarios. These examples should point to application specific challenges for the applied components and infrastructure, and it should emphasize the potential of multi-sensor systems and sensor data fusion.
The capabilities of non-destructive testing (NDT) methods for defect detection in civil engineering are characterized by their different penetration depth, resolution and sensitivity to material properties. Therefore, in many cases multi-sensor NDT has to be performed, producing large data sets that require an efficient data evaluation framework. In this work an image fusion methodology is proposed based on unsupervised clustering methods. Their performance is evaluated on ground penetrating radar and infrared thermography data from laboratory concrete specimens with different simulated near-surface defects. It is shown that clustering could effectively partition the data for further feature level-based data fusion by improving the detectability of defects simulating delamination, voids and localized water. A comparison with supervised symbol level fusion shows that clustering-based fusion outperforms this, especially in situations with very limited knowledge about the material properties and depths of the defects. Additionally, clustering is successfully applied in a case study where a multi-sensor NDT data set was automatically collected by a self-navigating mobile robot system.
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.
The aim of this work is to achieve reliable nondestructive evaluation (NDE) of adhesively bonded aerospace components by developing novel multidimensional data fusion techniques, which would combine the information obtained by ultrasonic and X-ray NDE methods. Separately, both NDE techniques have their advantages and limitations. The integration of data obtained from pulse echo immersion ultrasound testing and radiography holds immense potential to help improve the reliability of non-destructive evaluation. In this study, distinctive features obtained from single techniques, traditional ultrasonic pulse echo testing, and radiography, as well as fused images, were investigated and the suitability of these distinctive features and fusion techniques for improving the probability of defect detection was evaluated. For this purpose, aluminum single lap joints with brass inclusions were analyzed using ultrasound pulse echo and radiography techniques. The distinctive features were extracted from the data obtained, and images of features obtained by both techniques were fused together. Different combinations of features and fusion algorithms were investigated, considering the desire to automate data evaluation in the future.
LIBS‐ConSort: Development of a sensor‐based sorting method for construction and demolition waste
(2023)
AbstractA joint project of partners from industry and research institutions approaches the challenge of construction and demolition waste (CDW) sorting by investigating and testing the combination of laser‐induced breakdown spectroscopy (LIBS) with near‐infrared (NIR) spectroscopy and visual imaging. Joint processing of information (data fusion) is expected to significantly improve the sorting quality of various materials like concrete, main masonry building materials, organic components, etc., and may enable the detection and separation of impurities such as SO3‐cotaining building materials (gypsum, aerated concrete, etc.)Focusing on Berlin as an example, the entire value chain will be analyzed to minimize economic / technological barriers and obstacles at the cluster level and to sustainably increase recovery and recycling rates.The objective of this paper is to present current progress and results of the test stand development combining LIBS with NIR spectroscopy and visual imaging. In the future, this laboratory prototype will serve as a fully automated measurement setup to allow real‐time classification of CDW on a conveyor belt.
Non-destructive testing (NDT) of concrete structures is performed using pulse-echo methods. According to the type of the applied waves it can be classified as acoustic (Impact-Echo, Ultrasonics) and electromagnetic methods (Radar). The results are visualised through different imaging processes. This work is performed in the frame of a research project promoted by Deutsche Forschungsgemeinschaft (FOR384). The objective of data fusion is to use the complementary information of the different methods. Radar can detect metallic reflectors in concrete (metallic ducts and concrete reinforcement) very well. This method is not able to locate defects behind these reflectors (injecting defects, defects behind close concrete reinforcement), because the electromagnetic waves are completely reflected at metals. The acoustic methods are able to compensate this deficit i.e. Acoustic waves can penetrate through metal. But acoustic waves in the ultrasonic range are completely reflected by air layers. Air layers have smaller influence on radar propagation, so that both methods complement each other. In order to be able to combine the NDT-data from several methods records at the same volume, the different data setsmodes of signals must be adapted. The ultrasonic and radar data have to be reconstructed with programs based on the Synthetic Aperture Focusing Technique (SAFT) before data fusion. Subsequently, a conversion of the data into a uniform format has to be carried out. This is a prerequisite in order to keep the data exchange between the project partners as simple as possible. After the data sets are imported and transferred into a common reference system, they can be processed with operations according to the purpose of the investigation. Results, which have been achieved in concrete test specimen with radar and ultrasonics, will be presented and will show the feasibility of the data fusion method.
Results of reconstructed and fused NDT-data measured in the laboratory and on-site at bridges
(2006)
Non-destructive testing (NDT) of concrete structures plays an increasing role in civil engineering. This paper presents the results of measurements carried out in the laboratory at BAM and on-site at several bridges using reconstructed and fused radar and ultrasonic echo data sets. In this context different scanning systems, developed for the on-site application of NDT-methods (e.g. reinforced concrete bridges) are introduced. The main object was the demonstration of the improved effectiveness of radar and ultrasonic pulse echo technique due to the automated measurements and the application of new software for the data processing and data visualisation. The results of these measurements show the high potential of reconstruction and data fusion for the improvement and simplification of the interpretability of large data sets measured with impulse-echo methods.
Methods for the combination (i. e. comparison and overlay) and data fusion (i. e. integration of all data in one data set, replacement of data) of active thermography and D laser scanner (light section method) have been developed. Systematic investigations for quantification of damage in historic structures are presented using both techniques. A case study shows that reproducible investigations at regular time intervals are very well suited for structural monitoring.