Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery

Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery

Fault diagnosis plays an essential role in reducing the maintenance costs of rotating machinery manufacturing systems. In many real applications of fault detection and diagnosis, data tend to be imbalanced, meaning that the number of samples for some fault classes is much less than the normal data s...

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Bibliographic Details
Main Authors: Masoud Jalayer, Amin Kaboli, Carlotta Orsenigo, Carlo Vercellis
Format: Article
Language:English
Published: MDPI AG 2022-03-01
Series:Machines
Subjects:
fault detection
rotating machinery
condition monitoring
generative adversarial networks
signal processing
predictive maintenance
Online Access:https://www.mdpi.com/2075-1702/10/4/237

Internet

https://www.mdpi.com/2075-1702/10/4/237

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