An approach based on convolutional autoencoder for detecting damage location in a mechanical system

F. M. Bono; L. Radicioni; G. Bombaci; C. Somaschini; S. Cinquemani

doi:10.1117/12.2657974

25 April 2023 An approach based on convolutional autoencoder for detecting damage location in a mechanical system

F. M. Bono, L. Radicioni, G. Bombaci, C. Somaschini, S. Cinquemani

Proceedings Volume 12489, NDE 4.0, Predictive Maintenance, Communication, and Energy Systems: The Digital Transformation of NDE; 124890K (2023) https://doi.org/10.1117/12.2657974
Event: SPIE Smart Structures + Nondestructive Evaluation, 2023, Long Beach, California, United States

Conference Poster

Abstract

In the field of structural health monitoring, the adoption of intelligent systems able to automatically detect changes in a structure are evidently attractive. A change in the baseline configuration can be an early predictor of a structural defect that has to be monitored before it reaches critical conditions. When there is no prior knowledge on the system, deep learning models such as autoencoders could effectively detect a change and enhance the capability to determine the damage location. In this paper a deep learning approach is applied to a test rig consisting of a small building model composed by four floors connected by bending springs. Modifications of the system are simulated by changing stiffness of the spring. This algorithm is compared with traditional approach based on modal parameters by carrying out experimental tests to validate the hypothesis.

Citation Download Citation

F. M. Bono, L. Radicioni, G. Bombaci, C. Somaschini, and S. Cinquemani "An approach based on convolutional autoencoder for detecting damage location in a mechanical system", Proc. SPIE 12489, NDE 4.0, Predictive Maintenance, Communication, and Energy Systems: The Digital Transformation of NDE, 124890K (25 April 2023); https://doi.org/10.1117/12.2657974

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