Bearing fault diagnosis based on multi-index optimization TQWT sparse representation

QIANG Yudong; WAN Fangyi; ZHANG Chunlin; ZHANG Changxing

doi:10.16579/j.issn.1001.9669.2026.05.014

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Bearing fault diagnosis based on multi-index optimization TQWT sparse representation

·Vibration·Noise·Monitoring·Diagnosis· | 更新时间：2026-05-19

- Bearing fault diagnosis based on multi-index optimization TQWT sparse representation
- Journal of Mechanical Strength Vol. 48, Issue 5, Pages: 117-125(2026)
- 作者机构：
  
  1.西北工业大学航空学院，西安 710072
  2.西安电子科技大学先进材料与纳米科技学院，西安 710126
- 作者简介：
  
  WAN Fangyi, E-mail: fwan@nwpu.edu.cn
- 基金信息：
  
  National Natural Science Foundation of China(11972314)
- DOI：10.16579/j.issn.1001.9669.2026.05.014
  CLC： TH212;TH213.3
- Received：08 July 2024，
  
  Revised：2024-09-29，
  
  Published：15 May 2026
- 稿件说明：
移动端阅览
强煜栋，万方义，张春林，等. 基于多指标优化TQWT稀疏表示的轴承故障诊断［J］. 机械强度，2026，48（5）：117-125.

QIANG Yudong，WAN Fangyi，ZHANG Chunlin，et al. Bearing fault diagnosis based on multi-index optimization TQWT sparse representation［J］. Journal of Mechanical Strength，2026，48（5）：117-125.
强煜栋，万方义，张春林，等. 基于多指标优化TQWT稀疏表示的轴承故障诊断［J］. 机械强度，2026，48（5）：117-125. DOI： 10.16579/j.issn.1001.9669.2026.05.014.

QIANG Yudong，WAN Fangyi，ZHANG Chunlin，et al. Bearing fault diagnosis based on multi-index optimization TQWT sparse representation［J］. Journal of Mechanical Strength，2026，48（5）：117-125. DOI： 10.16579/j.issn.1001.9669.2026.05.014.

摘要

目的

针对轴承振动故障特征提取精度偏低、故障信息易被强噪声淹没、信号自适应构建缺乏合理评价指标以及数据量冗余等问题，开展轴承故障特征增强与故障诊断方法研究。提出了一种引入非凸正则项系数的稀疏分解方法，并基于多指标特征矩阵实现信号自适应重构。

方法

首先，通过tunable Q-factor 小波变换（Tunable Q-factor Wavelet Transform

TQWT）对信号进行稀疏分解，获得信号的最简表达，构造非凸惩罚函数以提升重构信号的稀疏性，同时保留关键故障特征信息；其次，综合谐波与噪声能量比、峭度偏斜度比及稀疏度构建融合指标参数矩阵，用于优选可调小波参数与稀疏惩罚项阈值；最后，利用优选参数完成信号去噪与重构，再对重构信号进行解调分析，提取轴承故障特征信息。

结果

仿真信号与试验结果表明，所提方法能够有效提取轴承早期故障冲击特征，实现轴承振动故障的准确诊断。

Abstract

Objective

Aiming at the problems of low precision in bearing vibration fault feature extraction

fault information easily submerged by strong noise

lack of reasonable evaluation indices for adaptive signal construction

and data redundancy

a study on bearing fault feature enhancement and fault diagnosis was conducted.

Methods

A sparse decomposition method with non-convex regularization coefficients wass proposed

and adaptive signal reconstruction is realized based on a multi-index feature matrix. Firstly

the signal is sparsely decomposed via tunable Q-factor wavelet transform (TQWT) to obtain its most concise representation. A non-convex penalty function is constructed to improve the sparsity of the reconstructed signal while retaining key fault feature information. Secondly

a fused index parameter matrix was established by integrating harmonic-to-noise energy ratio

kurtosis-skewness ratio

and sparsity

which was adopted to optimize the parameters of tunable wavelets and the threshold of sparse penalty terms. Finally

signal denoising and reconstruction were completed using the optimized parameters

and demodulation analysis was performed on the reconstructed signal to extract bearing fault feature information.

Results

Simulation and test results show that the proposed method can effectively extract the impact features of early bearing faults and achieve accurate diagnosis of bearing vibration faults.

关键词

Keywords

references

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