In many industrial sectors, structural health monitoring (SHM) is considered as an addition to nondestructive testing (NDT) that can reduce maintenance effort during the lifetime of a technical facility, structural component, or vehicle. A large number of SHM methods are based on ultrasonic waves, whose properties change depending on structural health. However, the wide application of SHM systems is limited due to the lack of suitable methods to assess their reliability. The evaluation of the system performance usually refers to the determination of the probability of detection (POD) of a test procedure. Up until now, only a few limited methods exist to evaluate the POD of SHM systems, which prevents them from being standardized and widely accepted in the industry. The biggest hurdle concerning the POD calculation is the large number of samples needed. A POD analysis requires data from numerous identical structures with integrated SHM systems. Each structure is then damaged at different locations and with various degrees of severity. All of these are connected to high costs. Therefore, one possible way to tackle this problem is to perform computer-aided investigations. In this work, the POD assessment procedure established in NDT according to the Berens model is adapted to guided wave-based SHM systems. The approach implemented here is based on solely computer-aided investigations. After efficient modeling of wave propagation phenomena across an automotive component made of a carbon-fiber-reinforced composite, the POD curves are extracted. Finally, the novel concept of a POD map is introduced to look into the effect of damage position on system reliability.

Data-driven analysis for damage assessment has a large potential in structural health monitoring (SHM) systems, where sensors are permanently attached to the structure, enabling continuous and frequent measurements. In this contribution, we propose a machine learning (ML) approach for automated damage detection, based on an ML toolbox for industrial condition monitoring. The toolbox combines multiple complementary algorithms for feature extraction and selection and automatically chooses the best combination of methods for the dataset at hand. Here, this toolbox is applied to a guided wave-based SHM dataset for varying temperatures and damage locations, which is freely available on the Open Guided Waves platform. A classification rate of 96.2% is achieved, demonstrating reliable and automated damage detection. Moreover, the ability of the ML model to identify a damaged structure at untrained damage locations and temperatures is demonstrated.

To assess the ability of structural health monitoring (SHM) systems, a variety of prerequisites and contributing factors have to be taken into account. Within this publication, this variety is analyzed for actively introduced guided wave-based SHM systems. For these systems, it is not possible to analyze their performance without taking into account their structure and their applied system parameters. Therefore, interdependencies of performance assessment are displayed in an SHM pyramid based on the structure and its monitoring requirements. Factors influencing the quality, capability and reliability of the monitoring system are given and put into relation with state-of-the-art performance analysis in a non-destructive evaluation. While some aspects are similar and can be treated in similar ways, others, such as location, environmental condition and structural dependency, demand novel solutions. Using an open-access data set from the Open Guided Waves platform, a detailed method description and analysis of path-based performance assessment is presented.The adopted approach clearly begs the question about the decision framework, as the threshold affects the reliability of the system. In addition, the findings show the effect of the propagation path according to the damage position. Indeed, the distance of damage directly affects the system performance. Otherwise, the propagation direction does not alter the potentiality of the detection approach despite the anisotropy of composites. Nonetheless, the finite waveguide makes it necessary to look at the whole paths, as singular phenomena associated with the reflections may appear. Numerical investigation helps to clarify the centrality of wave mechanics and the necessity to take sensor position into account as an influencing factor. Starting from the findings achieved, all the issues are discussed, and potential future steps are outlined.

Digital beamforming methods in plate–like structures are widely exploited for Lamb waves-based damage imaging. Among them, the Delay and Sum (DAS) imaging technique is the most popular thanks to its low computational cost and ease of implementation. However, the imaging outputs are low–quality due to the high levels of side lobes and limited off-axis signal rejection, which leads to limited image resolution and contrast. Recently, the Delay Multiply and Sum (DMAS) beamforming has been applied to NDT field as a promising DAS alternative able to enhance the imaging reconstruction in terms of contrast and damage detectability. However, DMAS is still affected by high levels of artefacts. To tackle this aspect, literature offers a beamforming algorithm called Double Stage DMAS (DS–DMAS), firstly introduced in photoacoustic imaging and medical ultrasound imaging. In this paper, the DS–DMAS performance is analysed for Lamb waves inspection, in order to provide an exhaustive comparison between DAS, DMAS and DS–DMAS. As a further step, a filtering process addressed as Fresnel Zone Filtering (FZF) is used to restrict the beamforming partial sums in a physical way to the area around the scattering point. The proposed approach is an adaptation of a well–established technique in seismic data processing called Fresnel migration, able to suppress artefacts and enhance the quality of the imaging. The algorithms have been compared and characterized by exploiting an online free data set for guided waves inspection (http://openguidedwaves.de/) which collects piezo pitch–catch signals travelling through a quasi-isotropic Carbon Fiber Reinforced Plate (CFRP) at different actuated frequencies and damage positions.