Zero-Shot Generative AI for Rotating Machinery Fault Diagnosis: Synthesizing Highly Realistic Training Data via Cycle-Consistent Adversarial Networks

Zero-Shot Generative AI for Rotating Machinery Fault Diagnosis: Synthesizing Highly Realistic Training Data via Cycle-Consistent Adversarial Networks

The Intelligent Fault Diagnosis of rotating machinery calls for a substantial amount of training data, posing challenges in acquiring such data for damaged industrial machinery. This paper presents a novel approach for generating synthetic data using a Generative Adversarial Network (GAN) with cycle...

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Bibliographic Details
Main Authors: Luigi Gianpio Di Maggio, Eugenio Brusa, Cristiana Delprete
Format: Article
Language:English
Published: MDPI AG 2023-11-01
Series:Applied Sciences
Subjects:
intelligent fault diagnosis
generative adversarial networks
cycleGANs
transfer learning
machine fault diagnosis
convolutional neural networks
Online Access:https://www.mdpi.com/2076-3417/13/22/12458

Internet

https://www.mdpi.com/2076-3417/13/22/12458

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