Unsupervised Classification and Remaining Useful Life Prediction for Turbofan Engines Using Autoencoders and Gaussian Mixture Models: A Comprehensive Framework for Predictive Maintenance
Unsupervised learning has emerged as a pivotal methodology in scenarios where labeled data is scarce, expensive, or impractical to obtain. This article presents a robust framework combining autoencoders and Gaussian Mixture Models (GMMs) for unsupervised classification and Remaining Useful Life (RUL...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/14/7884 |
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