Faults Diagnosis based on Autoencoder and Classifier Techniques
Author:
Affiliation:
1. Université de Lorraine, LCFC,Arts et Metiers Institute of Technology,Metz,France
2. ArcelorMittal Research SA,APRC,Maizières les Metz,France,57283
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10553373/10553653/10553914.pdf?arnumber=10553914
Reference22 articles.
1. Exponential discriminant analysis for fault diagnosis
2. Coupling data-driven and model-based methods to improve fault diagnosis
3. Independent component analysis application for fault detection in process industries: Literature review and an application case study for fault detection in multiphase flow systems
4. Isolation enhanced principal component analysis
5. Autoencoder-based representation learning and its application in intelligent fault diagnosis: A review
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