# Wear state identification of reciprocating sliding friction Pairs with frictional vibration

## TL;DR

This paper introduces a new method using fractal analysis and machine learning to accurately monitor the wear state of mechanical parts in real time.

## Contribution

The novel use of multifractal spectrum parameters with a nonlinear support vector machine for wear state identification in friction pairs.

## Key findings

- The proposed method achieves over 90% accuracy in identifying three wear states using 10-fold cross-validation.
- Multifractal parameters outperform traditional time-frequency and statistical features in characterizing wear states.
- The method provides a reliable approach for real-time online monitoring of mechanical wear.

## Abstract

Real-time monitoring of the wear state of reciprocating sliding friction pairs has long been a challenging issue. To address this problem, this paper innovatively proposes a new method of constructing feature vectors based on the fractal parameters of frictional vibration signals and employing a nonlinear support vector machine to identify different wear states. Three typical wear states, namely running-in wear, normal wear, and severe wear, were designed by adjusting the amount of lubricating oil and distinguished by variations in the friction coefficient. Unlike conventional time-frequency or statistical features, our approach uniquely employs multifractal spectrum parameters to characterize wear states. The research results demonstrate that this method achieves recognition accuracies exceeding 90% for all three wear states in 10-fold cross-validation, indicating the effectiveness of the nonlinear support vector machine in realizing the recognition of different wear states of reciprocating sliding friction pairs. This achievement not only provides a new technical approach for online monitoring of wear states but also offers a valuable reference for the application of nonlinear signal analysis in other fields.