Summary of De-sate: Denoising Self-attention Transformer Encoders For Li-ion Battery Health Prognostics, by Gaurav Shinde et al.
De-SaTE: Denoising Self-attention Transformer Encoders for Li-ion Battery Health Prognostics
by Gaurav Shinde, Rohan Mohapatra, Pooja Krishan, Saptarshi Sengupta
First submitted to arxiv on: 28 Sep 2023
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 A novel approach to predicting the Remaining Useful Life (RUL) of Lithium-ion (Li-ion) batteries is proposed, utilizing multiple denoising modules to address various types of noise in battery data. The method combines a denoising auto-encoder and wavelet denoiser with self-attention transformer encoders to generate encoded representations, which are then processed to estimate health indicator values under diverse noise patterns. Experimental results on NASA and CALCE datasets demonstrate the approach’s effectiveness, achieving error metrics comparable to or better than state-of-the-art methods reported in recent literature. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Li-ion batteries are used in many devices, from phones to cars. One challenge is predicting when they will stop working well. This study uses special tools called denoising modules to clean up noisy data and make a better guess about how long the battery will last. They test their method on real data from NASA and another group and get results that are just as good or even better than other methods used in the past. |
Keywords
* Artificial intelligence * Encoder * Self attention * Transformer
