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

TL;DR

This study compares the importance of feature extraction versus classification algorithms in vibration signal fault diagnosis, highlighting which component has a greater impact on prediction accuracy in machine learning models.

Contribution

The paper provides an experimental analysis of the relative importance of feature extraction and classification algorithms in vibration signal diagnosis tasks.

Findings

Feature extraction based on Gaussian models and statistical characteristics impacts performance.

Different classification algorithms have varying effects on prediction accuracy.

The study identifies the more critical component for effective vibration fault diagnosis.

Abstract

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.