Modal filtering has numerous applications in the analysis of object dynamics. One possible use of this technique, recently presented in the literature, is damage detection. The method has several advantages but one major disadvantage — it does not provide any information about damage location. This article presents a solution that eliminates this drawback. The proposed method is tested first on a numerical example. Next, it is verified on the laboratory measurement data, and finally, it is applied to a real structure — a footbridge over a street in Krakow.

In this paper the authors propose a modification to the known force identification procedure based on modal filtration. The modification consists of replacing the modal vectors with the Ritz vectors. The latter seem to be more accurate because they take into account the static deformation of the structure and are less sensitive to truncation error. After the main idea- the algorithm modification- is presented, it is verified and then compared with the original solution. Two sets of data are used for this purpose, firstly simulation data from the numerical model, and then physical data recorded during a laboratory experiment.

Guided waves are a potent tool in structural health monitoring, with promising machine learning algorithm applications due to the complexity of their signals. However, these algorithms usually require copious amounts of data to be trained. Collecting the correct amount and distribution of data is costly and time-consuming, and sometimes even borderline impossible due to the necessity of introducing damage to vital machinery to collect signals for various damaged scenarios. This data scarcity problem is not unique to guided waves or structural health monitoring, and has been partly addressed in the field of computer vision using generative adversarial neural networks. These networks generate synthetic data samples based on the distribution of the data they were trained on. Though there are multiple researched methods for simulating guided wave signals, the problem is not yet solved. This work presents a generative adversarial network architecture for guided waves generation and showcases its capabilities when working with a series of pitch-catch experiments from the OpenGuidedWaves database. The network correctly generates random signals and can accurately reconstruct signals it has not seen during training. The potential of synthetic data to be used for training other algorithms was confirmed in a simple damage detection scenario, with the classifiers trained exclusively on synthetic data and evaluated on real signals. As a side effect of the signal reconstruction process, the network can also compress the signals by 98.44% while retaining the damage index information they carry.