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Unsupervised anomaly detection for industrial data using Generative Adversarial Networks

Year: 2025

Type: conference-paper

DOI: 10.1109/iemdc60492.2025.11061107

Authors: Michel Lehmann, Andreas Möckel

Institution: Technische Universität Ilmenau

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: closed

OpenAlex ID: W4412021477

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Cites: 14

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Related to: 10