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Generative modelling of vibration signals in machine maintenance
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University of Technology and Humanities in Radom, Poland
Submission date: 2023-07-31
Final revision date: 2023-09-12
Acceptance date: 2023-10-07
Online publication date: 2023-10-12
Publication date: 2023-10-12
Corresponding author
Andrzej Adam Puchalski   

University of Technology and Humanities in Radom, ul.Malczewskiego 29, 26-600, Radom, Poland
Eksploatacja i Niezawodność – Maintenance and Reliability 2023;25(4):173488
  • Generative models gaining increasing use in various fields, including machinery operation,are presented.
  • A VAE variational autoencoder was proposed as a tool for generating measurement observations for vibration monitoring of rotating machinery to complement unbalanced databases.
  • The algorithm was optimised and verified in a practical solution to the task of generating data for intermediate states of a demonstration gearbox.
The exponential development of technologies for the acquisition, collection, and processing of data from real-world objects is creating new perspectives in the field of machine maintenance. The Industrial Internet of Things is the source of a huge collection of measurement data. The performance of classification or regression algorithms needs to take into account the random nature of the process being modelled and any incomplete observability, especially in terms of failure states. The article highlights the practical possibilities of using generative artificial intelligence and deep machine learning systems to create synthetic measurement observations in monitoring the vibrations of rotating machinery to improve unbalanced databases. Variational Autoencoder VAE generative models with latent variables in the form of high-level input features of time-frequency spectra were studied. The mapping and generation algorithm was optimised and its effectiveness was tested in the practical solution of the task of diagnosing the three operating states of a demonstration gearbox.
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