A Novel k-Nearest Neighbor Method with Distribution Discrepancy and Differential Feature Importance for Rolling Bearing Fault Diagnosis

Zhenghui Li; Na Zhang; Ziming Wang

doi:10.3791/70568

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Method Article

A Novel k-Nearest Neighbor Method with Distribution Discrepancy and Differential Feature Importance for Rolling Bearing Fault Diagnosis

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DOI:

10.3791/70568

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June 5th, 2026

Zhenghui Li¹ , Na Zhang¹ , Ziming Wang¹

¹Electrical Engineering, Zhengzhou Railway Vocational & Technical College

Summary

This study proposes a k-nearest neighbor method that integrates distribution discrepancy and differential feature importance for accurate rolling bearing fault diagnosis.

Abstract

Rolling bearings are among the most vulnerable components in various types of rotary machinery, and accurate fault detection and localization are essential. When a rolling bearing fails, the signal is non-stationary, and the energy distribution of the vibration signal varies depending on the fault location. In traditional k-nearest neighbor (KNN) fault diagnosis algorithms, Euclidean distance is primarily used to measure the distance between sample points, which is not effective at capturing similarity across different spatial distributions. Moreover, these algorithms assume equal feature importance, which does not reflect the actual characteristics of fault vibration signals. This study proposes a KNN-based rolling bearing fault diagnosis method that incorporates distribution discrepancy and differential feature importance. First, vibration signals are decomposed using three-level wavelet packet decomposition, and the energy of each node at the third level is used as the fault feature. Then, the mean impact value (MIV) algorithm is used to determine the relative importance of each feature, and the Earth mover’s distance (EMD) is applied to measure differences between spatial distributions. By integrating Euclidean distance with MIV and EMD and applying the KNN majority voting rule, fault diagnosis is performed. The experimental results indicate that this method achieves a diagnostic accuracy of 99.43%, representing a 5.97% improvement compared to traditional KNN methods. The proposed method demonstrates accurate and effective fault diagnosis performance on the rolling bearing datasets used in this study.

Introduction

With the progress of technology, rotary machinery is increasingly developing toward integration, large-scale systems, high speed, and intelligent operation¹. Among various types of rotary electrical machinery, rolling bearing components are the most vulnerable and commonly damaged parts. They offer advantages such as easy start-up, low friction, simple lubrication, and convenient replacement, and are widely used in precision instruments, aerospace, automobiles, machine tools, robots, and other fields. According to relevant statistical data on rotating machinery failures, abnormal vibration faults account for 70%, and 30% of these vibration faul....