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Performance assessment for GuidedWave (GW)-based Structural Health Monitoring (SHM) systems is of major importance for industrial deployment.
With conventional feature extraction methods like damage indices, pathbased probability of detection (POD) analysis can be realized. To achieve reliability quantification enough data needs to be available, which is rarely the case.
Alternatives like methods for performance assessment on system level are still in development and in a discussion phase. In this contribution, POD results using an Artificial Intelligence (AI)-based data analysis are compared with those delivered by conventional data analysis. Using an open-access dataset from Open Guided Wave platform, the possibility of performance assessment for GW-based SHM systems using AI-based data analysis is shown in detail. An artificial neural network (ANN) classifier is trained to detect artificial damage in a stiffened CFRP plate. As input for the ANN, classical damage indicators are used. The ANN is tested to detect damage at another position, whose inspection data were not previously used in training. The findings show very high detection capabilities without sorting any specific path but only having a global view of current damage metrics. The systematic evaluation of the ANN predictions with respect to specific damage sizes allows to compute a probability of correct identification versus flaw dimension, somehow equivalent to and compared with the results achieved through classic path-based POD analysis. Also, sensitive paths are detected by ANN predictions allowing for evaluation of maximal distances between path and damage position. Finally, it is shown that the prediction performance of the ANN can be improved significantly by combining different damage indicators as inputs.
Reliability assessment of Structural Health Monitoring (SHM) systems
poses new challenges pushing the research community to address many questions which are still open. For guided wave-based SHM it is not possible to evaluate the system performance without taking into account the target structure and applied system parameters. This range of variables would result in countless measurements.
Factors like environmental conditions, structural dependencies and wave characteristics demand novel solutions for performance analysis of SHM systems compared to those relying on classical non-destructive evaluation. Such novel approaches typically require model-assisted investigations which may not only help to explain and understand performance assessment results but also enable complete studies without costly experiments. Within this contribution, a multi input multi output approach using a sparse transducer array permanently installed on a composite structure to excite and sense guided waves is considered. Firstly, the method and the analysis of path-based performance assessment are presented considering an open-access dataset from the Open Guided Wave platform. Then, a performance analysis of a guided wave-based SHM system using Probability of Detection is presented. To explain some unexpected results, the model-assisted investigations are used to understand the physical phenomena of wave propagation in the test specimen including the interaction with damage. Finally, issues and future steps in SHM systems’ performance assessment and their development are discussed.
The baroque epitaph Reyer (1704) at the St. Laurentius church in Tönning was treated with carbolineum as a wood preservation agent in 1903. The subsequent constant migration of carbolineum through the layers of paint to the surface has had a detrimental effect on the aesthetic appearance of the epitaph. Carbolineum is an oily, water-insoluble, flammable, dark brown mixture of coal tar oil components. Due to its content of polycyclic aromatic hydrocarbons (PAH), which are classified as carcinogenic and harmful to the environment, the use of carbolineum has been forbidden. The aim of the project is to develop an exemplary Conservation treatment (hat will reduce the toxic contents of the historical wooden object. The new method should be applicable to similar objects that are also contaminated with carbolineum. The historic carbolineum was identified by the GC-MS method and the X-Ray computed tomography revealed the biological wood damage and previous restoration measures. Several decontamination methods such as the extraction with supercritical C02, with dichloromethane vapour/fluids, with 1,3-dioxolane or with petrol ether by vaccum impregnation were investigated. This project is founded by the Deutsche Bundesstiftung Umwelt (DBU).