Rolling bearing fault diagnosis of agricultural machinery equipment based on the improved EWT algorithm

LU Yunfei; U Yongchang; WANG Yu; SHEN Tao

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Rolling bearing fault diagnosis of agricultural machinery equipment based on the improved EWT algorithm

更新时间：2026-10-16

- Rolling bearing fault diagnosis of agricultural machinery equipment based on the improved EWT algorithm
- Journal of Mechanical Transmission Pages: 1-9(2026)
- 作者机构：
  
  1.河南农业职业学院机电工程系，郑州 451450
  2.河南农业大学机电工程学院，郑州 451450
- 作者简介：
- 基金信息：
- DOI：
  CLC： TH133.33
- Received：07 April 2026，
  
  Revised：2026-06-15，
  
  Accepted：16 June 2026，
  
  Online First：13 October 2026，
- 稿件说明：
移动端阅览
卢云飞，余泳昌，王煜，等.基于改进EWT算法的农机设备滚动轴承故障诊断［J］.机械传动，XXXX，XX（XX）：1-9.

LU Yunfei，U Yongchang，WANG Yu，et al.Rolling bearing fault diagnosis of agricultural machinery equipment based on the improved EWT algorithm［J］.Journal of Mechanical Transmission，XXXX，XX（XX）：1-9.
卢云飞，余泳昌，王煜，等.基于改进EWT算法的农机设备滚动轴承故障诊断［J］.机械传动，XXXX，XX（XX）：1-9. DOI：

LU Yunfei，U Yongchang，WANG Yu，et al.Rolling bearing fault diagnosis of agricultural machinery equipment based on the improved EWT algorithm［J］.Journal of Mechanical Transmission，XXXX，XX（XX）：1-9. DOI：

摘要

目的

针对农机设备滚动轴承低速重载工况下振动信号频带划分易受低频背景能量干扰、故障诊断精度与稳定性下降的问题，提出一种谱平滑与低频能量增强协同优化的改进经验小波变换（Smoothed Energy-Weighted Empirical Wavelet Transform

SEW-EWT）算法。

方法

首先，对原始信号进行傅里叶变换获取幅值谱，采用高斯核卷积平滑抑制高频随机尖峰，基于平滑谱局部极值初定频带边界，引入能量比例阈值剔除低能量伪分段，提升频带划分稳定性；其次，在保持滤波器组能量守恒前提下，构造低频加权增强函数，对低频区间赋予更高权重，强化弱冲击特征的模态能量占比，实现关键分量自适应纯化；然后，选取有效模态进行希尔伯特变换并提取包络谱特征，为故障状态判别提供输入；最后，基于包含4种运行状态的滚动轴承试验数据集验证算法诊断性能。

结果

结果表明，SEW-EWT的精确率、召回率与F1-score分别达97.2%、97.8%和97.5%，较传统EWT提升约3.8%；误差均方值稳定在0.03左右，较基线方法下降约30%；识别准确率均值达97.75%，标准差降至0.58%，较EWT降低约66.3%；与主流诊断方法相比，整体准确率高出3.25~8.0个百分点，各类故障识别表现均衡。该算法可提升低速工况下轴承故障特征可辨识性与诊断一致性，为农机设备滚动轴承状态监测提供参考。

Abstract

Objective

Under low-speed and heavy-load conditions

the frequency band division of vibration signals of rolling bearings in agricultural machinery equipment is easily disturbed by low-frequency background energy

which reduces the accuracy and stability of fault diagnosis. To solve this problem

an improved empirical wavelet transform (SEW-EWT) algorithm with collaborative optimization of spectral smoothing and low-frequency energy enhancement was proposed.

Methods

Firstly

the amplitude spectrum was obtained by Fourier transform of the original signal. Gaussian kernel convolution smoothing was adopted to suppress high-frequency random spikes. The frequency band boundaries were initially determined based on local extrema of the smoothed spectrum

and an energy proportion threshold was introduced to eliminate low-energy pseudo-segments

so as to improve the stability of frequency band division. Secondly

on the premise of maintaining the energy conservation of the filter bank

a low-frequency weighted enhancement function was constructed. Higher weights were assigned to the low-frequency interval to strengthen the modal energy proportion of weak impact features and realize adaptive purification of key components. Then

Hilbert transform was performed on the selected effective modes and envelope spectrum features were extracted to provide input for fault state discrimination. Finally

the diagnostic performance of the algorithm was verified based on a rolling bearing test dataset containing 4 operating states.

Results

The results show that the precision

recall and F1-score of SEW-EWT reach 97.2%

97.8% and 97.5% respectively

which are about 3.8% higher than those of traditional EWT. The mean square error is stable at about 0.03

which is about 30% lower than that of the baseline method. The average recognition accuracy reaches 97.75%

and the standard deviation drops to 0.58%

which is about 66.3% lower than that of EWT. Compared with mainstream diagnostic methods

the overall accuracy is 3.25 to 8.0 percentage points higher

with balanced recognition performance for various faults. This algorithm can improve the identifiability of bearing fault features and diagnostic consistency under low-speed conditions

and provides reference for condition monitoring of rolling bearings in agricultural machinery equipment.

关键词

Keywords

references

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