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Measuring the moisture content of floor screeds is usually done with minor destructive testing methods like Darr drying or the Calcium Carbid (CM) method. These require small samples, deliver only punctual information and still have proven not to be very reliable. Hence, a study has been made using the standard destructive tests as well as a suite of non-destructive testing methods working out their use for moisture determination. In this study five partners from research institutes and industry worked together and intensively researched different technologies. The main focus was put on the varying sensitivity of the measuring techniques in different moisture ranges. Especially for low moisture contents ( or ‘critical’ moisture contents when the screed is dry enough to be covered with the final floor finish), several commercial devices including the most commonly used CM-method failed to determine the correct moisture content for cementitious samples. Hence the need for more accurate, if possible non-destructive methods is high, taking also into account that the chemistry (and physical properties) of screeds may vary strongly depending on their origin and purpose.
Control and data acquisition of automated multi-sensor systems - two examples from civil engineering
(2010)
Data fusion for multi-sensor nondestructive detection of surface cracks in ferromagnetic materials
(2018)
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.
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.
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.