Research on Gear Fault Diagnosis Method based on Local Characteristic Scale Decomposition and Mathematical Morphological Filtering

Jiaxing Qi; Weicheng Cui; Xiuping Zhao

doi:10.16578/j.issn.1004.2539.2019.07.025

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Research on Gear Fault Diagnosis Method based on Local Characteristic Scale Decomposition and Mathematical Morphological Filtering

Test·Analysis | 更新时间：2022-09-24

- Research on Gear Fault Diagnosis Method based on Local Characteristic Scale Decomposition and Mathematical Morphological Filtering
- Journal of Mechanical Transmission Vol. 43, Issue 7, Pages: 140-145(2019)
- 作者机构：
  
  1.海军航空大学岸防兵学院，山东烟台 264001
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2019.07.025
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齐嘉兴,崔伟成,赵修平.基于局部特征尺度分解和数学形态滤波的齿轮故障诊断方法研究[J].机械传动,2019,43(07):140-145.

Qi Jiaxing,Cui Weicheng,Zhao Xiuping.Research on Gear Fault Diagnosis Method based on Local Characteristic Scale Decomposition and Mathematical Morphological Filtering[J].Journal of Mechanical Transmission,2019,43(07):140-145.
齐嘉兴,崔伟成,赵修平.基于局部特征尺度分解和数学形态滤波的齿轮故障诊断方法研究[J].机械传动,2019,43(07):140-145. DOI： 10.16578/j.issn.1004.2539.2019.07.025.

Qi Jiaxing,Cui Weicheng,Zhao Xiuping.Research on Gear Fault Diagnosis Method based on Local Characteristic Scale Decomposition and Mathematical Morphological Filtering[J].Journal of Mechanical Transmission,2019,43(07):140-145. DOI： 10.16578/j.issn.1004.2539.2019.07.025.

摘要

为了实现齿轮故障的精确诊断，针对齿轮早期故障振动信号非线性、非平稳且信噪比低的特点，提出了一种基于局部特征尺度分解（LCD）和数学形态滤波的齿轮故障诊断方法。首先，对齿轮振动信号进行局部特征尺度分解，得到若干内禀尺度分量（ISC）；然后，依据峭度准则，选取峭度最大的ISC作为故障特征分量，再运用形态差值滤波器对其进行滤波；最后，对滤波结果求取频谱并进行故障诊断。通过对仿真结果和实验数据的分析，说明了该方法的可行性和有效性。结果表明，该方法具有抑制噪声和提取故障冲击特征的能力，能够有效地实现齿轮故障的精确诊断。

Abstract

In order to accurately diagnose gear faults, a gear fault diagnosis method based on local characteristic scale decomposition (LCD) and mathematical morphological filtering is proposed, which aims at the non-linear, non-stationary and low signal-to-noise ratio characteristics of vibration signals of early gear faults. Firstly, some Intrinsic Scale Components (ISCs) are obtained by local characteristic scale decomposition of gear vibration signals. Then, according to kurtosis criterion, the ISC with the largest kurtosis is selected as the fault characteristic component, and then filtered by morphological difference filter. Finally, the spectrum of the filtering results is obtained and the fault diagnosis is carried out. The feasibility and validity of the method are illustrated by analyzing the simulation results and experimental data. The results show that the method has the ability to suppress noise and extract fault shock characteristics, and can effectively realize the accurate diagnosis of gear fault.

关键词

局部特征尺度分解数学形态滤波齿轮故障诊断

Keywords

Local characteristic scale decompositionMathematical morphological filteringGearFault diagnosis

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