Transformer-Based GAN with Multi-STFT for Rotating Machinery Vibration Data Analysis

Prognostics and health management of general rotating machinery have been studied over time to improve system stability. Recently, the excellent abnormal diagnosis performance of artificial intelligence (AI) was demonstrated, and therefore, AI-based intelligent diagnosis is now being implemented in...

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Bibliographic Details
Published in:Electronics (Basel) 2024-11, Vol.13 (21), p.4253
Main Authors: Lee, Seokchae, Jeong, Hoejun, Kwon, Jangwoo
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Computational linguistics
Data analysis
Datasets
Deep learning
Diagnosis
Electric transformers
Fourier transforms
Generative adversarial networks
Language processing
Liquors
Machine learning
Machinery
Natural language interfaces
Natural language processing
Neural networks
Performance enhancement
Rotating machinery
Systems stability
Vibration
Vibration analysis
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Summary:Prognostics and health management of general rotating machinery have been studied over time to improve system stability. Recently, the excellent abnormal diagnosis performance of artificial intelligence (AI) was demonstrated, and therefore, AI-based intelligent diagnosis is now being implemented in these systems. AI models are trained using large volumes of data. Therefore, we propose a transformer-based generative adversarial network (GAN) model with a multi-resolution short-time Fourier transform (multi-STFT) loss function to augment the vibration data of rotating machinery to facilitate the successful learning of deep learning models. We constructed a model with a conditional GAN structure, which is transformer based, for learning the feature points of vibration data in the time-series domain. In addition, we applied the multi-STFT loss function to capture the frequency features of the vibration data. The generated data, which adequately captured the frequency features, were used to augment the training data to improve the performance of a deep learning classifier. Furthermore, by visualizing the generated vibration data and comparing the visualizations to those of the vibration data obtained from real machinery, we demonstrated that the generated data were indistinguishable from the actual data.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13214253