A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

Barbara Zaparoli Cunha , Christophe Droz (1) , Abdel-Malek Zine (2, 3) , Stéphane Foulard (4) , Mohamed Ichchou (5)

1 I4S - Statistical Inference for Structural Health Monitoring
2 ICJ - Institut Camille Jordan
3 MMCS - Modélisation mathématique, calcul scientifique
4 Compredict GmbH [Darmstadt]
5 LTDS - Laboratoire de Tribologie et Dynamique des Systèmes

The use of Machine Learning (ML) has rapidly spread across several fields of applied sciences, having encountered many applications in Structural Dynamics and Vibroacoustic (SD&V). An advantage of ML algorithms compared to traditional techniques is that physical phenomena can be modeled using only sampled data from either measurements or simulations. This is particularly important in SD&V when the model of the studied phenomenon is either unknown or computationally expensive to simulate. This paper presents a survey on the application of ML algorithms in three classical problems of SD&V: structural health monitoring, active control of noise and vibration, and vibroacoustic product design. In structural health monitoring, ML is employed to extract damage-sensitive features from sampled data and to detect, localize, assess, and forecast failures in the structure. In active control of noise and vibration, ML techniques are used in the identification of state-space models of the controlled system, dimensionality reduction of existing models, and design of controllers. In vibroacoustic product design, ML algorithms can create surrogates that are faster to evaluate than physics-based models. The methodologies considered in this work are analyzed in terms of their strength and limitations for each of the three considered SD&V problems. Moreover, the paper considers the role of digital twins and physics-guided ML to overcome current challenges and lay the foundations for future research in the field.

Mots clés: Machine Learning; Structural Health Monitoring; Surrogate Model; Active Vibration Control; Active Noise Control; Digital-Twin; Physics Guided Machine Learning

Domaines: Acoustique [physics.class-ph]; Apprentissage [cs.LG]; Systèmes et contrôle [cs.SY]; Vibrations [physics.class-ph]

Barbara Zaparoli Cunha, Christophe Droz, Abdel-Malek Zine, Stéphane Foulard, Mohamed Ichchou. A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic. Mechanical Systems and Signal Processing, 2023, 200, pp.110535. ⟨10.1016/j.ymssp.2023.110535⟩. ⟨hal-03563614v3⟩