基于磁通门传感器与CEEMDAN-DWT的滑油磨粒信号提取方法

薛倩; 吴志鹏; 尹海成

doi:10.13224/j.cnki.jasp.20240785

基于磁通门传感器与CEEMDAN-DWT的滑油磨粒信号提取方法

doi: 10.13224/j.cnki.jasp.20240785

中国民航大学电子信息与自动化学院，天津 300300

基金项目: 天津市教委科研计划项目（XJ2023006901）

详细信息

作者简介:
薛倩（1987-），女，副教授，博士，研究领域为航空无损检测和电学层析成像。E-mail：qxue@cauc.edu.cn

通讯作者:
吴志鹏（1999-），男，硕士生，研究领域为航空无损检测。E-mail：wzp18047264627@163.com

中图分类号: V233.4
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出版历程
- 收稿日期: 2024-11-19
- 网络出版日期: 2026-05-30

Method for extracting signal of lubricating oil wear debris based on fluxgate sensor and CEEMDAN-DWT

College of Electronic Information and Automation，Civil Aviation University of China，Tianjin 300300，China

摘要

摘要:
针对滑油磨粒的在线监测技术是发动机部件磨损故障预测和评估的关键，本文设计基于磁通门原理的新型磁感应式传感器，将磨粒引起的微小磁场扰动转化为磁感应强度变化，从而获得可测的感应电压信号。针对磨粒有效信号微弱的问题，提出基于完全集成经验模态分解与自适应噪声算法（CEEMDAN）和离散小波变化（DWT）的磨粒微弱磁异常信号提取方法。CEEMDAN算法通过添加自适应白噪声，有效解决了经验模态分解（EMD）中的模态混叠问题，提高了分解的准确性和稳定性；同时，结合DWT的多尺度分析能力，能够准确捕捉并提取隐藏在强噪声背景中的微弱磁异常信号。样机实验结果表明：靠近管道中间通过的磨粒（距离传感器最远，信噪比最低）信号经过提取后，信噪比在1～10 dB范围内得到不同程度的增强。在信号保真度为0.6～0.9的基础上，去噪增益达到0.5～1。
- 航空发动机磨损故障 /
- 磁感应式传感器 /
- 滑油磨粒监测 /
- 磁通门 /
- 信号提取
Abstract:
Online monitoring technology for oil debris is crucial for predicting and assessing wear failures in engine components. This paper designed a novel magnetic inductive sensor based on the principle of fluxgate. It converted the minute magnetic disturbances caused by wear debris into magnetic induction intensity variations, and further into measurable induced voltage signals. To detect the weak debris signals effectively, we proposed a method combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and discrete wavelet transform (DWT) for extracting weak magnetic anomaly signals of debris. The CEEMDAN algorithm addressed the mode mixing issue in empirical mode decomposition (EMD) by adding adaptive white noise, thereby enhanced the accuracy and stability of decomposition. In addition, with the multi-scale analysis capability of DWT, the weak magnetic anomaly signals buried in noise were captured and extracted effectively. Prototype experiment results indicated that after extraction, the measured data of wear debris passing near the center of the pipeline (which is further from the sensor and has lower SNR) exhibited SNR enhancements ranging from 1 to 10 dB. With a signal fidelity range of 0.6 to 0.9, the denoising gain ranges from 0.5 to 1.
- aero-engine wear fault /
- magnetic inductive sensor /
- lubricating oil wear debris monitoring /
- fluxgate /
- signal extraction

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图 1 传统磁通门传感器模型

Figure 1. Conventional fluxgate sensor model

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图 2 第4、5个周期内的感应电压和频谱

Figure 2. Induced voltage and spectrum during the 4th and5th periods

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图 3 感应线圈电压频谱偶次谐波幅值

Figure 3. Even harmonic amplitude of the induction coil voltage spectrum

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图 4 传感器数学模型

Figure 4. Sensor mathematical model

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图 5 磁通门式滑油磨粒传感器

Figure 5. Fluxgate sensor for lubricating oil wear debris

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图 6 铁磨粒引起的变化和磨粒与传感器垂直距离的关系

Figure 6. Relationship between the change caused by ferromagnetic debris and the perpendicular distance from the debris to the sensor

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图 7 实验测试系统

Figure 7. Experimental test system

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图 8 铁磁性磨粒实验信号

Figure 8. Ferromagnetic debris signals in experiments

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图 9 非铁磁性磨粒实验信号

Figure 9. Nonferromagnetic debris signals in experiments

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图 10 三维空间坐标模型

Figure 10. Three-dimensional spatial coordinate model

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图 11 基于CEEMDAN-DWT的信号去噪处理方法框图

Figure 11. Block diagram of signal denoising processing method based on CEEMDAN-DWT

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图 12 CEEMDAN算法的分解结果

Figure 12. Decomposition results of the CEEMDAN

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图 13 信噪比评价指标建立方法框图

Figure 13. Block diagram for establishing SNR evaluation metrics

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图 14 较快速度实验中划分范围对信噪比的影响

Figure 14. Impact of range division on SNR in high-speed experiments

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图 15 较慢速度实验中划分范围对信噪比的影响

Figure 15. Impact of range division on SNR in low-speed experiments

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图 16 划分不同IMF分量对信噪比的影响

Figure 16. Impact of dividing different IMF components on SNR

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图 17 实测数据和提取算法处理对比示意图

Figure 17. Comparison diagram of measured data and processed data using extraction algorithm

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表 1 上方传感器输出电压信号对比

Table 1. Comparison of upper sensor output voltage signal

磨粒直径/μm	磨粒通过位置	快速通过/mV	慢速通过/mV
250～380	上	11.5	22.5
250～380	中	0.9	1.62
106～180	上	4.83	12.3
106～180	中	0.86	0.99
75	上	1.95	7.49
75	中	0.74	0.8

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表 2 下方传感器输出电压信号对比

Table 2. Comparison of lower sensor output voltage signal

磨粒直径/μm	磨粒通过位置	快速通过/mV	慢速通过/mV
250～380	下	6.8	9.2
250～380	中	1.6	1.8
106～180	下	3.4	3.2
106～180	中	1.1	1.2
75	下	1.3	1.8
75	中	0.6	0.8

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表 3 实验数据分类

Table 3. Classification of experimental data

磨粒速度/（m/s）	磨粒直径/μm	实验数据
磨粒速度/（m/s）	磨粒直径/μm	图8（a）	图8（b）	图8（c）	图8（d）
0.2	250～380	Data 1	Data 4	Data 7	Data 10
0.05	106～180	Data 2	Data 5	Data 8	Data 11
0.05	75	Data 3	Data 6	Data 9	Data 12

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