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This talk demonstrates the results of the IGSTC-project entitled "NDT-Data Fusion".
Project approach:
Nondestructive testing (NDT) of concrete buildings allows to coordinate efficient repair measures. Multi-sensor platforms collect large data sets. Nevertheless, data analysis is typically performed manually. Data Fusion uses the full potential of a multi-sensory data set in order to:
improve information quality (reliability, robustness, accuracy, clarity, completeness) and enables automated algorithm based data analysis.
We present the project achievements, namely:
- Development of building scanner system for multisensory NDT
- Laboratory multi sensor investigations
- Development of data fusion concept for honeycombing and pitting corrosion
- Field testing
Half-cell potential mapping (HP) is the most popular nondestructive test (NDT)-method for the localization of corrosion damage in concrete. It is generally recognized, that HP is prone to the environmental factors that arise from salt induced deterioration, such as varying moisture and chloride gradients. Additional NDT-methods are capable to determine distinctive areas, but cannot yet be used to estimate more accurate testing results. We introduce a supervised machine learning (SML) based approach for data fusion to make use of the additional sensor information. SML are methods that explore relations between different (sensor) data from predefined data labels. We use a simple linear classifier named logistic regression to distinguish defect and intact areas. The test performance improves drastically compared to the best single method, HP. In order to generate representative, labeled data we conducted a comprehensive experiment that simulates the deterioration-cycle of a chloride-exposed building part in the lab. Our data set consist of 18 measurement campaigns, each containing HP-, ground-penetrating-radar-, microwave-moisture-, and Wenner-resistivity-data. We detail the challenges that arise with a data driven approach in NDT and how we addressed them.
The use of duplex stainless steels (DSS) in energy related applications is well known. Nowadays, DSS steels become more favorable than austenitic steels due to the outstanding mechanical properties, the good corrosion resistance and the lower nickel content. However, the use of the duplex grade in acidic environments such as seawater often leads to severe degradation of the structural integrity of the steel by hydrogen-induced/assisted cracking (HAC) phenomena, which can eventually result in premature failure. Hydrogen assisted degradation and cracking of steels are active fields of research even though this topic is intensively studied for more than a century. A bottleneck is the analytical validation of the theoretical models proposed ion the literature at the sub-micron scale.
Industrial and the research communities see a need for an accurate analytical method by which it is possible to image the distribution of hydrogen in the microstructure of a steels or and other alloys. Among the very few available methods hydrogen imaging methods, Time-of-Flight secondary ion mass spectrometry (ToF-SIMS) has the principal capability for mapping of hydrogen in a steel’s microstructure. The combination of ToF-SIMS with multivariate data analysis (MVA), electron microscopy (SEM) and electron-backscattered diffraction (EBSD) is a powerful approach for providing chemical and structural information. The use of data fusion techniques has been shown recently to enhance the better understanding of the hydrogen induced degradation processes in in a DSS steel.