A bearing fault feature extraction method based on digital signal processing
基于数字信号处理的轴承复合故障特征提取方法

摘要

本文提出一种基于数字信号处理的轴承复合故障特征提取方法，通过对轴承振动信号进行多尺度时频分析与特征分解，实现复合故障特征的有效分离与识别。方法利用小波包分解、经验模态分解及瞬时频率提取技术，强化信号中微弱故障特征的表达，并结合统计特征量进行特征优化，提高复合故障诊断的准确性和鲁棒性。仿真及实验结果表明，该方法在复杂运行环境下能够稳定捕捉多类型故障信息，为轴承状态监测提供可靠数据支撑。

Abstract

This paper proposes a digital signal processing-based method for composite fault feature extraction in bearings. By conducting multi-scale time-frequency analysis and feature decomposition on bearing vibration signals, the method achieves effective separation and identification of composite fault characteristics. Utilizing wavelet packet decomposition, empirical mode decomposition, and instantaneous frequency extraction techniques, the approach enhances the representation of weak fault features in signals. Combined with statistical feature quantization, it optimizes features to improve diagnostic accuracy and robustness. Simulation and experimental results demonstrate that this method can stably capture multiple types of fault information under complex operating conditions, providing reliable data support for bearing condition monitoring.