Summary of One Masked Model Is All You Need For Sensor Fault Detection, Isolation and Accommodation, by Yiwei Fu et al.
One Masked Model is All You Need for Sensor Fault Detection, Isolation and Accommodation
by Yiwei Fu, Weizhong Yan
First submitted to arxiv on: 24 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 novel framework proposes a masked model approach for sensor fault detection, isolation, and accommodation (FDIA) using self-supervised learning, applicable to any neural network capable of sequence modeling. It captures complex spatio-temporal relationships among sensors by creating random masks simulating faults during training. The proposed technique demonstrates effectiveness on public and real-world datasets from GE offshore wind turbines, outperforming existing approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to detect and fix faulty sensors in engineering systems like wind turbines. It uses special models that learn from data without needing labels. This approach can find faulty sensors and correct them, making sensor measurements more accurate and reliable. The technique is tested on real-world data from GE offshore wind turbines and performs better than existing methods. |
Keywords
* Artificial intelligence * Neural network * Self supervised
