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Almost all engineering disciplines face the problem of damage detection and assessment. Although the terminology might change between disciplines, the problems faced, and the methods of solution, show great commonality. The objective of this paper is to discuss how Structural Health Monitoring SHM is carried out in the context of aerospace and civil structural monitoring and to draw parallels with how damage detection is conducted within the electrical, control and process engineering communities.

A four-stage methodology for SHM, based on machine learning is discussed.

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The different levels of diagnostic information available depend on the type of data available for learning and this is also discussed. In this way, the acquisition process was made by looking the effect of the attenuation with long cables 2. These experiments are explained below. First experiment: acquisition performed with a short cable 0.

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Second experiment: acquisition performed with long cable to sensors 2. Third experiment: acquisition performed with a short cable 0. As it was previously introduced, in the first group of experiments, the influence of added noise to the data will be explored in order to determine how it affects the results in the principal components.

For this, the Golay filter is applied to reduce the influence of aleatory signals and after the white Gaussian noise is added to the signals. Later, the methodology was applied to the signals with and without noise to determine the influence of the white noise in the detection process. An example of the signals used by the algorithms in the actuation phase 2 can be seen in Figure 7 , similar results are obtained with all the signals. Signal received by sensors in the first experiment, without damage a with Golay filter applied without white Gaussian noise b with Golay filter applied with white Gaussian noise.

This behavior is the same in all the actuation phases. First two principal components for experiment 1: a without added noise b with 25dB of white Gaussian noise. As seen in Figure 8a and 8b , the first the principal components are able to eliminate the noise and prove that they are a good tool for defining the elements to include in the machine this is the experiment one. After searching the principal components, the machines are trained with these data.

Structural health monitoring: a machine learning perspective | Brunel University

Although all the machine learning methods were explored, following worst and best results are shown for a better understanding. Figure 9 shows the confusion matrix with test Coarse KNN machine, and the result in all cases was very poor, with most machines having this behavior. In general, the response of these machine learning algorithms was good with or without added noise because PCA has shown great ability to reject the noise. The second case was considered when the acquisition system is connected with long cables, and Golay filter for pre-processing is used, in this case the signals in some cases were bad digitalized because of the impedance of cable, the noise, the low voltage of the stimulus, and other experimental features.

An example of the captured signals is shown in Figure Figure 12 shows the first two principal components obtained from the signal, which were used to train the machines. As in the previous experiment, all the methods were explored and best and worst results are included in this work. Figure 13 shows the confusion matrix with Weighted KNN, and the behavior was similar to the first experiment. Bad results were obtained with other methods for Coarse KNN. Figure 14 shows this behavior, which is similar to the experiment 1.

Similar results were obtained with the third experiment; in this case, a short cable was used and unfiltered signals were used to calculate the scores. Figure 15 shows the acquired signal in the actuation phase 1. Figure 16 shows the first two principal components of the signal, however in this experiment these data were not used to train the machines, this means, principal components are projected into the machines trained in the first experiment to determine the influence of these changes in the results.

Figure 17 shows the response of the Coarse KNN machine, in this last case, the training is not success with this data series. Figure 18 shows the response of the Fine KNN machine, similar results to the previous case are obtained, this means, a bad classification is provided by the machine. The piezoelectric transducers working as an active inspection system provide a good system to produce mechanical waves over materials under evaluation. In all the cases, the information obtained from the healthy state and the different damage scenarios applied to the methodology showed that algorithm is available to detect real and simulated damages in both structures in spite of shapes and differences in the element under inspection.

Others types of machines did not work well for the experiments. In all cases, it is necessary to train the machines with data pre-processed in the same way as in the definition of the healthy state, changes in the elements such as the cable length and the use of the Golay filter are enough to change the results in the PCA model obtained which do that the machines do not work correctly.

PCA is a robust mechanism to characterize data since it was demonstrated to eliminate the noise, however, more experiments need to be considered by including environmental and operational noise to determine the effectiveness of the algorithm. Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3. Help us write another book on this subject and reach those readers. Login to your personal dashboard for more detailed statistics on your publications. Edited by Srinivasan Ramakrishnan.

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