Summary of Generation Of Uncorrelated Residual Variables For Chemical Process Fault Diagnosis Via Transfer Learning-based Input-output Decoupled Network, by Zhuofu Pan et al.
Generation of Uncorrelated Residual Variables for Chemical Process Fault Diagnosis via Transfer Learning-based Input-Output Decoupled Network
by Zhuofu Pan, Qingkai Sui, Yalin Wang, Jiang Luo, Jie Chen, Hongtian Chen
First submitted to arxiv on: 29 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed transfer learning-based input-output decoupled network (TDN) combines an input-output decoupled network (IDN) and a pre-trained variational autoencoder (VAE) for diagnostic purposes. IDN generates uncorrelated residual variables through diagonalization and parallel computing operations, while VAE guides the training of IDN by providing knowledge of normal status according to loss and maximum mean discrepancy loss. The trained IDN learns to map faulty to normal states, serving as both fault detection index and estimated fault signal simultaneously. This approach is verified through a numerical example and chemical simulation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new method for diagnostic purposes that combines deep learning and traditional decoupling techniques. It uses a transfer learning-based network to learn the mapping from faulty to normal states, which can be used for both fault detection and estimation. The method is shown to be effective through numerical examples and chemical simulations. |
Keywords
» Artificial intelligence » Deep learning » Transfer learning » Variational autoencoder
