PSI - Issue 37

Deniss Mironovs et al. / Procedia Structural Integrity 37 (2022) 410–416 Deniss Mironovs/ Structural Integrity Procedia 00 (2019) 000 – 000

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5. Conclusion A CFRP laminated composite beam structure was modelled in ANSYS software package. Modal analysis of the beam was performed resulting in 5 modes, represented by modal frequencies and mode shape vectors. Gaussian noise was added to the modal data set, creating 5000 independent samples. Additionally, different delamination was introduced into the beam model, giving 4 extra modal data samples. A machine learning algorithm called anomaly detection was implemented to 1) train the statistical model how the reference healthy state is represented by frequencies and mode shapes; 2) set the threshold between known healthy and damaged states; 3) validate the algorithm by using independent testing data set of 1002 samples (1000 healthy, 2 damaged).

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Fig. 3. a) Probability of frequency values for healthy and damaged states for 5 th mode; b) multivariate ( ) for 1 st mode; c) multivariate ( ) for 1 st mode as function of frequency. The algorithm has shown promising results, as it correctly distinguishes healthy and damaged states, even in cases where modal frequencies are indistinguishable between mentioned states. It has been noted that FE modelling program output format for mode shapes can influence the result. More research is planned for the topic, specifically to add mode shape preprocessing to reduce number of variables in the data set for faster and more reliable algorithm execution, as well as for reduction of probability scale. Acknowledgement This work has been supp orted by the European Regional Development Fund within the Activity 1.1.1.2 “Post doctoral Research Aid” of the Specific Aid Objective 1.1.1 “To increase the research and innovative capacity of scientific institutions of Latvia and the ability to attract external financing, investing in human resources and f, Hz