Machine Learning Applications to Vibration Problems

A special issue of Vibration (ISSN 2571-631X).

Deadline for manuscript submissions: closed (20 July 2025) | Viewed by 1951

Special Issue Editor


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Guest Editor
Department of Aerospace Engineering, San Jose State University, San Jose, CA 95192, USA
Interests: finite elements; machine learning; mobile sensors; response monitoring; stress reconstruction; safety; structural health monitoring

Special Issue Information

Dear Colleagues,

Machine learning and data-driven algorithms are promising approaches in analysing the dynamic behaviour of a mechanical system. These algorithms have the ability to automatically generate a model using data from past experiences; the number of applications is extensive and includes self-driving cars, high-frequency trading, house price estimation, search engines, bioinformatics, chemistry, and material science, for which large amounts of data are available. Cases involving large amounts of variables, high levels of uncertainty, and rapid changes in behaviour are among the typical scenarios. Although machine learning algorithms date back to the 1950s, their application for analysing the mechanical behaviour of dynamic systems has only been the focus of research for the past 10 years and is now progressing very quickly.

This Special Issue aims to collect the latest research findings in the field and invites the submission of articles related (but not limited) to the following topics:

  • Surrogate machine learning approaches for the stress analysis of vibrating systems;
  • Data-driven approaches for structural health monitoring;
  • Machine learning approaches for sensor optimization in vibration analysis;
  • Data-driven real-time stress predictions;
  • Machine learning applications in relation to finite element analysis for vibrating systems;
  • Uncertainty quantification in the stress analysis of machine learning modelling;
  • Data-driven modal analysis;
  • Physics-informed machine learning approaches for vibrating systems.

Dr. Maria Chierichetti
Guest Editor

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Keywords

  • machine learning approaches
  • data-driven modelling
  • stress analysis
  • surrogate models
  • structural health monitoring
  • finite elements
  • modal analysis

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Published Papers (2 papers)

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Research

28 pages, 7417 KB  
Article
Prediction of Local Vibration Analysis for Ship Stiffened Panel Structure Using Artificial Neural Network Algorithm
by Mahardika Rizki Pynasti and Chang-Yong Song
Vibration 2025, 8(3), 52; https://doi.org/10.3390/vibration8030052 (registering DOI) - 13 Sep 2025
Abstract
Ship stiffened panels, typically flat plates reinforced with various types of stiffeners, form a substantial part of a ship’s structure and are susceptible to resonance, especially in areas such as the after peak structure, engine room, and accommodation compartments. These vibrations are primarily [...] Read more.
Ship stiffened panels, typically flat plates reinforced with various types of stiffeners, form a substantial part of a ship’s structure and are susceptible to resonance, especially in areas such as the after peak structure, engine room, and accommodation compartments. These vibrations are primarily excited by main engine and propeller forces. Conventional methods such as finite element analysis (FEA) and plate theory are widely used to estimate vibration frequencies, but they are time-consuming and computationally intensive when applied to numerous stiffened panels. This study proposes a machine learning approach using an artificial neural network (ANN) algorithm to efficiently predict the vibration frequencies of ship stiffened panels. A crude oil tanker is chosen as the case study, and FEA is conducted to generate the vibration frequency and mass data for panels across critical regions. The input layer features for the ANN include panel area, thickness, number and area of stiffeners, fluid density, number of fluid contact sides, and overall structural stiffness. The ANN model predicts two outputs: the fundamental vibration frequency and the mass of the panel structure. To evaluate the model performance, hyperparameters such as the number of hidden neurons are optimized. The results indicate that the ANN achieves accurate predictions while significantly reducing the time and resources required compared with conventional methods. This approach offers a promising tool for accelerating the local vibration analysis process in ship structural design. Full article
(This article belongs to the Special Issue Machine Learning Applications to Vibration Problems)
17 pages, 3642 KB  
Article
Crack Location in Wind Turbine Blades Using Vibration Signal and Support Vector Machine
by Perla Y. Sevilla-Camacho, José B. Robles-Ocampo, Juvenal Rodríguez-Resendíz, Sergio De la Cruz-Arreola, Marco A. Zuñiga-Reyes and Edwin N. Hernández-Estrada
Vibration 2025, 8(2), 20; https://doi.org/10.3390/vibration8020020 - 21 Apr 2025
Cited by 1 | Viewed by 1092
Abstract
This study introduces a new method to locate cracks in wind turbine blades using the support vector machine algorithm and the tangential vibration signal measured at the root blade in static conditions. The method was implemented in hardware and experimentally validated on 200 [...] Read more.
This study introduces a new method to locate cracks in wind turbine blades using the support vector machine algorithm and the tangential vibration signal measured at the root blade in static conditions. The method was implemented in hardware and experimentally validated on 200 W wind turbine blades. The blade conditions were healthy, and transverse cracked at the root, midsection, and tip. The experimental procedure is easy, and only one low-cost piezoelectric accelerometer is needed, which is affordable and straightforward to install. The machine learning technique used requires a small dataset and low computing power. The results show exceptional performance, achieving an accuracy of 99.37% and a precision of 98.77%. This approach enhances the reliability of wind turbine blade monitoring. It provides a robust early detection and maintenance solution, improving operational efficiency and safety in wind energy production. K-nearest neighbors and decision trees are also used for comparison purposes. Full article
(This article belongs to the Special Issue Machine Learning Applications to Vibration Problems)
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