Generative modelling of vibration signals in machine maintenance

• Autorzy: Puchalski Andrzej Adam , Komorska Iwona

Identyfikatory

DOI

10.17531/ein/173488

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

time-frequency analysis; condition monitoring; anomalies detection; deep generative models; variational autoencoder; data distribution

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2023
• Tom: Vol. 25, no. 4
• Strony: art. no. 173488
• Opis fizyczny: Bibliogr. 31 poz., tys., wykr.

Twórcy

autor

Puchalski Andrzej Adam

• University of Technology and Humanities in Radom, Poland

• https://orcid.org/0000-0002-5202-3410

autor

Komorska Iwona

• University of Technology and Humanities in Radom, Poland

• https://orcid.org/0000-0002-6911-0297

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