Applications of generative models with a latent observation subspace in vibrodiagnostics

• Autorzy: Puchalski Andrzej , Komorska Iwona

Identyfikatory

DOI

10.29354/diag/176854

• Języki publikacji: EN

Abstrakty

EN

The vibration signal is one of the most essential diagnostic signals, the analysis of which allows for determining the dynamic state of the monitored machine set. In the era of cyber-physical industrial systems, making diagnostic decisions involves the study of large databases from previous registers and data downloaded from machines in real-time. However, the recorded signals mainly concern the operational status of the monitored object. Insufficient training data regarding failure states hinders the operation of classification algorithms. Progress in machine learning has created a new avenue for the advancement of diagnostic methods based on models. These methods now have the capability to produce signals through random sampling from a hidden space or generate fresh instances of input data from noise. The article suggests the use of a Generative Adversarial Network (GAN) model as a tool to create synthetic measurement observations for vibration monitoring. The effectiveness of the synthetic data generation algorithm was verified on the example of the vibration signal recorded during tests of the drive system of a motor vehicle.

Słowa kluczowe

EN

vibration signal; deep neural network; generative adversarial network; GAN model; synthetic subspace

PL

sygnał wibracyjny; głęboka sieć neuronowa; GAN; wibrodiagnostyka

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2023
• Tom: Vol. 24, No. 4
• Strony: art. no. 2023413
• Opis fizyczny: Bibliogr. 19 poz., rys., tab.

Twórcy

autor

Puchalski Andrzej

• Casimir Pulaski Radom University, Mechanical Faculty, Stasieckiego 54, 26-600 Radom, Poland

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

autor

Komorska Iwona

• Casimir Pulaski Radom University, Mechanical Faculty, Stasieckiego 54, 26-600 Radom, Poland

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

Bibliografia

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