基于故障特征显著性指数图的滚动轴承最优解调频带识别方法

doi:10.3901/JME.2025.23.108

机械工程学报 ›› 2025, Vol. 61 ›› Issue (23): 108-119.doi: 10.3901/JME.2025.23.108

• 机械动力学 • 上一篇

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基于故障特征显著性指数图的滚动轴承最优解调频带识别方法

郑近德^1,2, 丁文海^1,2, 程健^1,2, 李姜宏^1,2, 桑炜^1,2

1. 安徽工业大学机械工程学院马鞍山 243032;
2. 安徽省智能破拆装备工程实验室马鞍山 243032

收稿日期:2024-06-06 修回日期:2024-12-27 发布日期:2026-01-22
作者简介:郑近德(通信作者),男,1986年出生,博士,教授,博士研究生导师。主要研究方向为设备状态监测与故障诊断、信号处理和复杂性理论。E-mail:lqdlzheng@126.com
丁文海,男,1999年出生,硕士研究生。主要研究方向为设备状态监测与故障诊断。E-mail:whd2472934691@163.com
程健,男,1995年出生,博士,讲师。主要研究方向为设备状态监测与故障诊断、信号处理。E-mail:chengjian@ahut.edu.cn
基金资助:
国家自然科学基金(52475080, 51975004);安徽省高校杰青科研(2022AH020032)资助项目

The FCSIgram-based Identification Method of Optimal Demodulation Frequency Band for Rolling Bearing

ZHENG Jinde^1,2, DING Wenhai^1,2, CHENG Jian^1,2, LI Jianghong^1,2, SANG Wei^1,2

1. School of Mechanical Engineering, Anhui University of Technology, Maanshan 243032;
2. Anhui Province Engineering Laboratory of Intelligent Demolition Equipment, Maanshan 243032

Received:2024-06-06 Revised:2024-12-27 Published:2026-01-22

摘要/Abstract

摘要： 基于共振解调的滚动轴承故障诊断方法的关键是选择最优解调频带。针对快速谱峭度图(Fast Kurtogram,FK)方法中固定频带划分方式的不足，提出了一种新的自适应共振解调方法—故障特征显著性指数图(Fault characteristic saliency index gram,FCSIgram)。FCSIgram首先基于快速迭代滤波(Fast iterative filtering,FIF)算法提取滚动轴承频域信号的谱趋势曲线；其次，通过调整FIF算法中滤波区间的长度，建立多级谱分割模型，实现对频谱的多级自适应划分；然后，基于振动信号的多点峭度谱构建故障特征显著性指数指标，并用于选择滚动轴承振动信号的最优解调频带；最后，对所选频带进行带通滤波，结合平方包络解调分析，实现滚动轴承故障特征的提取与诊断。将所提方法应用于滚动轴承故障仿真及实测信号分析，并与现有Kurtogram、Autogram及Infogram等同类方法进行对比，结果表明，所提方法不但能够准确定位轴承故障信号的最优解调频带，而且能够稳定实现故障特征提取与诊断。

关键词: 共振解调, 最优解调频带, 快速迭代滤波, 滚动轴承, 故障诊断

Abstract: The key of fault diagnosis method of rolling bearing based on resonance demodulation is to select the optimal demodulation frequency band. Aiming at the deficiency of the fixed frequency band division in Fast Kurtogram (FK) method for optimal frequency band demodulation, a novel adaptive resonance demodulation method named Fault characteristic saliency index gram (FCSIgran) is proposed. First, FCSIgran extracts the spectrum trend curve of frequency domain signal of bearing based on Fast iterative filtering (FIF) algorithm. Second, the multilevel spectrum segmentation model can be established by adjusting the filter interval size in FIF algorithm, so as to realize multilevel adaptive spectrum segmentation. Then, the fault characteristic saliency index indicator is constructed based on the Multipoint kurtosis spectrum of vibration signal, and which is used to select the optimal demodulation frequency band of bearing vibration signal. Finally, conducting band-pass filtering on the selected frequency band, and combining with square envelope demodulation analysis, the fault feature extraction and diagnosis of rolling bearing are achieved. The proposed method is applied to analyze the fault simulation and measured signals of rolling bearing with comparing with the similar methods such as Kurtogram, Autogram, and Infogram, the results show that the proposed method can not only accurately locate the optimal demodulation frequency band of the signals from faulty bearing, but also stably achieve fault feature extraction and diagnosis.

Key words: resonance demodulation, optimal demodulation frequency band, fast iterative filtering, rolling bearing, fault diagnosis

中图分类号:

TH165

郑近德, 丁文海, 程健, 李姜宏, 桑炜. 基于故障特征显著性指数图的滚动轴承最优解调频带识别方法[J]. 机械工程学报, 2025, 61(23): 108-119.

ZHENG Jinde, DING Wenhai, CHENG Jian, LI Jianghong, SANG Wei. The FCSIgram-based Identification Method of Optimal Demodulation Frequency Band for Rolling Bearing[J]. Journal of Mechanical Engineering, 2025, 61(23): 108-119.

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基于故障特征显著性指数图的滚动轴承最优解调频带识别方法

The FCSIgram-based Identification Method of Optimal Demodulation Frequency Band for Rolling Bearing

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