Summary of Can Transformers Predict Vibrations?, by Fusataka Kuniyoshi et al.
Can Transformers Predict Vibrations?
by Fusataka Kuniyoshi, Yoshihide Sawada
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to predict vibrations in electric vehicles (EVs) using a transformer-based model called Resoformer. The study focuses on predicting torsional resonance, which occurs when the interaction between motor and tire vibrations causes excessive loads on the vehicle’s drive shaft. Current damping technologies only detect resonance after the vibration amplitude reaches a certain threshold, leading to significant loads at detection time. Resoformer utilizes time-series of motor rotation speed as input and predicts the amplitude of torsional vibration at a specified quantile occurring in the shaft after the input series. The model improves accuracy by calculating attention between recursive and convolutional features extracted from measured data points. Experiments conducted on strong baselines built on the VIBES dataset demonstrate that Resoformer achieves state-of-the-art results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Electric vehicles can experience vibrations when driving on rough terrain, which can cause excessive loads on the drive shaft. Researchers are working to predict these vibrations using a new model called Resoformer. This model is based on transformers and uses information about motor rotation speed to forecast how strong the vibrations will be in the future. The study tested this model on a dataset of 2,600 simulated vibration sequences and found that it performed better than other models. |
Keywords
* Artificial intelligence * Attention * Time series * Transformer
