Applications of generative models with a latent observation subspace in vibrodiagnostics
1
Casimir Pulaski Radom University, Mechanical Faculty, Stasieckiego 54, 26-600 Radom, Poland
These authors had equal contribution to this work
Submission date: 2023-10-17
Final revision date: 2023-12-01
Acceptance date: 2023-12-12
Online publication date: 2023-12-12
Publication date: 2023-12-12
Corresponding author
Andrzej Puchalski
Casimir Pulaski Radom University, Mechanical Faculty, Stasieckiego 54, 26-600 Radom, Poland
Casimir Pulaski Radom University, Mechanical Faculty, Stasieckiego 54, 26-600 Radom, Poland
Diagnostyka 2023;24(4):2023413
KEYWORDS
TOPICS
ABSTRACT
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.
FUNDING
no founding
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