# Feature extraction and classification algorithm, which one is more essential? An experimental study on a specific task of vibration signal diagnosis

arXiv: https://arxiv.org/abs/2112.09389 · HTML: https://mlsanity.com/paper/2112.09389/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 40 references that are in the map.
- First posted on arXiv 17 Dec 2021.

## Builds on (most cited first, 2 of 2)

- [Stochastic Configuration Networks: Fundamentals and Algorithms](https://mlsanity.com/paper/1702.03180/) (2017)
- [A comprehensive review on convolutional neural network in machine fault diagnosis](https://mlsanity.com/paper/2002.07605/) (2020)

## Abstract (arXiv)

With the development of machine learning, a data-driven model has been widely used in vibration signal fault diagnosis. Most data-driven machine learning algorithms are built based on well-designed features, but feature extraction is usually required to be completed in advance. In the deep learning era, feature extraction and classifier learning are conducted simultaneously, which will lead to an end-to-end learning system. This paper explores which one of the two key factors, i.e., feature extraction and classification algorithm, is more essential for a specific task of vibration signal diagnosis during a learning system is generated. Feature extractions from vibration signal based on both well-known Gaussian model and statistical characteristics are discussed, respectively. And several classification algorithms are selected to experimentally validate the comparative impact of both feature extraction and classification algorithm on prediction performance.