Summary of Generative Adversarial Wavelet Neural Operator: Application to Fault Detection and Isolation Of Multivariate Time Series Data, by Jyoti Rani and Tapas Tripura and Hariprasad Kodamana and Souvik Chakraborty
Generative adversarial wavelet neural operator: Application to fault detection and isolation of multivariate time series data
by Jyoti Rani, Tapas Tripura, Hariprasad Kodamana, Souvik Chakraborty
First submitted to arxiv on: 8 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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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 A novel deep learning approach called GAWNO (Generative Adversarial Wavelet Neural Operator) is proposed for fault detection and isolation in complex systems. This unsupervised method combines wavelet neural operators and generative adversarial networks to capture temporal distributions and spatial dependencies among variables. The approach consists of two stages: training the GAWNO on normal operating conditions, and then using a reconstruction error-based threshold to detect and isolate faults based on discrepancy values. The proposed method is validated using three datasets: Tennessee Eastman Process, Avedore wastewater treatment plant (WWTP), and N2O emissions named as WWTPN2O. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of detecting and fixing problems in complex systems has been developed. It uses a special kind of artificial intelligence called GAWNO. This helps find issues by looking at how different things are related to each other over time. The method is divided into two parts: learning what normal behavior looks like, and then finding faults by seeing how much something differs from the expected pattern. |
Keywords
* Artificial intelligence * Deep learning * Unsupervised
