基于改进完备集成经验模态分解的钢丝绳缺陷漏磁检测方法

钟小勇; 陈科安; 张小红

doi:10.13272/j.issn.1671-251x.2022020037

基于改进完备集成经验模态分解的钢丝绳缺陷漏磁检测方法

江西理工大学理学院, 江西赣州　341000

基金项目: 国家自然科学基金项目(51665019, 61763017)；江西省研究生创新专项资金项目（YC2020-S479）。

详细信息

作者简介:
钟小勇（1964—），男，江西遂川人，教授级高级工程师，硕士，主要研究方向为安全智能诊断与无损检测、嵌入式系统及应用，E-mail：zhongxy52@jxust.edu.cn

中图分类号: TD534.6
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出版历程
- 收稿日期: 2022-02-19
- 修回日期: 2022-07-07
- 网络出版日期: 2022-05-18
- 刊出日期: 2022-08-08

Steel wire rope defect magnetic flux leakage detection method based on improved complementary ensemble empirical mode decomposition

School of Science, Jiangxi University of Science and Technology, Ganzhou 341000, China

摘要

摘要: 钢丝绳小缺陷信号往往被淹没在股波噪声中，存在钢丝绳小缺陷检测困难、易漏检等问题。针对该问题，提出了一种基于改进完备集成经验模态分解（ICEEMD）的钢丝绳缺陷漏磁检测方法。为了避免钢丝绳表面润滑剂或尘埃对检测信号造成影响，采用电磁检测法。将ICEEMD、小波阈值滤波（WTF）、维纳滤波（WF）相结合，得到ICEEMD−WTF−WF多级降噪方法：通过ICEEMD分解钢丝绳漏磁信号，得到本征模态函数（IMF）分量；计算IMF分量的能量比、排列熵、互相关系数，取出IMF趋势分量和IMF股波噪声分量，并对股波噪声分量进行WTF，筛选有用的IMF分量重构信号；对重构后的信号进行WF，去除随机噪声。提取降噪后的缺陷特征值，输入BP神经网络并进行训练，识别钢丝绳缺陷漏磁信号。实验结果表明：ICEEMD−WTF−WF多级降噪方法对钢丝绳漏磁信号具有良好的降噪效果，信噪比、峭度指标优于WTF、移动平均滤波和WF；基于ICEEMD−WTF−WF的BP神经网络模型检测耗时短，对小缺陷的平均准判率达到98.13%，能较好地满足钢丝绳缺陷检测要求。
- 钢丝绳 /
- 小缺陷检测 /
- 漏磁检测 /
- 改进完备集成经验模态分解 /
- 小波阈值滤波 /
- 维纳滤波 /
- 多级降噪
Abstract: The signal of small defects in steel wire rope is often submerged in wave noise. Therefore, it is difficult to detect small defects in wire rope and easy to miss detection. In order to solve this problem, a steel wire rope defect magnetic flux leakage detection method based on improved complementary ensemble empirical mode decomposition (ICEEMD) is proposed. To avoid the influence of the lubricant or dust on the surface of the wire rope on the detection signal, the electromagnetic detection method is adopted. CEEMD-WTF-WF multi-stage noise reduction method is obtained by combining ICEEMD, wavelet threshold filtering (WTF) and Wiener filtering (WF). The intrinsic mode function (IMF) component is obtained by decomposing the magnetic flux leakage signal of steel wire rope through ICEEMD. The energy ratio, permutation entropy and cross-correlation coefficient of IMF components are calculated. The IMF trend component and IMF stock noise component are extracted. WTF is conducted on the stock noise component to filter the useful IMF component reconstruction signal. WF is applied to the reconstructed signal to remove random noise. The eigenvalues of the de-noised defects are extracted, input and trained by BP neural network. The magnetic flux leakage signals of the steel wire rope defects are identified. The experimental results show that ICEEMD-WTF-WF multi-stage noise reduction method has good noise reduction effect on the magnetic flux leakage signal of steel wire rope. The SNR and kurtosis indexes are better than those of WTF, moving average filter and WF. The BP neural network model based on ICEEMD-WTF-WF takes a short time to detect. The average accuracy rate of small defects reaches 98.13%, which can better meet the requirements of wire rope defect detection.
- steel wire rope /
- small defect detection /
- magnetic flux leakage detection /
- improved complementary ensemble empirical mode decomposition /
- wavelet threshold filtering /
- Wiener filtering /
- multi-stage noise reduction

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图 1 钢丝绳缺陷漏磁检测系统

Figure 1. Magnetic flux leakage detection system for steel wire rope defects

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图 2 钢丝绳小缺陷漏磁信号

Figure 2. Magnetic leakage signal of small defects in steel wire rope

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图 3 IMF分量

Figure 3. IMF components

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图 4 IMF2分量降噪前的信号

Figure 4. IMF2 component signal before denoising

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图 5 IMF2分量降噪后的信号

Figure 5. IMF2 component signal after denoising

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图 6 钢丝绳缺陷漏磁信号

Figure 6. Magnetic flux leakage signal of steel wire rope defect

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图 7 基于ICEEMD−WTF−WF的误差收敛曲线

Figure 7. Error convergence curves based on ICEEMD−WTF−WF

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图 8 基于ICEEMD−WTF−WF和WTF的分类误差对比

Figure 8. Classification error comparison between ICEEMD−WTF−WF and WTF

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图 9 基于WF和移动平均滤波的分类误差对比

Figure 9. Classification error comparison between WF and moving average filtering

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表 1 各IMF分量的互相关系数、排列熵

Table 1 The cross-correlation coefficient and permutation entropy of each IMF component

IMF分量	互相关系数	排列熵	IMF分量	互相关系数	排列熵
IMF1	0.4947	0.9940	IMF5	0.0172	0.5932
IMF2	0.9313	0.9538	IMF6	0.0024	0.4048
IMF3	0.2773	0.8591	IMF7	0.0048	0.4181
IMF4	0.1058	0.6942	IMF8	0.0066	0

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表 2 各IMF分量所占的能量比

Table 2 Energy ratio of each IMF component

IMF分量	能量值	能量比/%	IMF分量	能量值	能量比/%
IMF1	1.75×10⁷	5.82	IMF5	3.98×10⁵	0.13
IMF2	2.58×10⁸	85.86	IMF6	4.45×10⁵	0.15
IMF3	1.95×10⁷	6.49	IMF7	9.31×10⁴	0.03
IMF4	4.34×10⁶	1.44	IMF8	2.10×10⁵	0.07

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表 3 不同滤波方法对比

Table 3 Comparison of different filtering methods

滤波方法	信噪比	互相关系数	均方根误差	峭度
ICEEMD−WTF−WF	20.152	0.5263	508.74	84.240
WTF	19.507	0.3728	543.90	59.205
移动平均滤波	8.406	0.3065	292.99	5.184
WF	11.637	0.6771	271.28	20.063

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表 4 训练样本数据（部分）

Table 4 Training sample data (part)

波峰值	波谷值	峰峰值	波宽	波形下面积	波形能量
0.4791	0.2293	0.3160	0.6000	0.4397	0.2847
1.0000	0.0194	1.0000	0.1667	1.0000	0.2482
0.8683	0.0000	0.9265	0.1667	0.9889	0.2468
0.6300	0.0209	0.7707	0.4667	0.6229	0.5045
0.5597	0.1426	0.6778	0.3333	0.5554	0.2376
0.7838	0.0107	0.8699	0.2000	0.9774	0.2976
0.5895	0.0844	0.7198	0.2667	0.6363	0.2288
0.2641	0.6434	0.2913	0.9333	0.2705	0.2767
0.5521	0.1183	0.4903	0.9000	0.5779	1.0000
0.5700	0.1566	0.6784	0.5333	0.6919	0.7089
0.4439	0.4651	0.4750	0.4667	0.4361	0.2014
0.5935	0.0554	0.7275	0.6000	0.7443	0.8808
0.5574	0.1102	0.6744	0.6333	0.6867	0.8025
0.5568	0.1111	0.6885	0.7000	0.6771	0.9242
0.6425	0.1992	0.7059	0.3000	0.6985	0.3022

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表 5 基于4种滤波方法的小缺陷准判率对比

Table 5 Comparison of small defect accuracy rate based on four filtering methods

滤波方法	检测时间/s	特征数据/组	测试数据/组	检测正确组数	准判率/%
ICEEMD−WTF−WF	0.2230	258	150	147	98.00
WTF	0.2449	258	150	128	85.33
移动平均滤波	0.2174	258	150	132	88.00
WF	0.2363	258	150	109	72.67

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表 6 多组实验数据实验结果对比

Table 6 Comparison of experimental results of multiple sets of experimental data %

实验序号	准判率
实验序号	ICEEMD−WTF−WF	WTF	移动平均滤波	WF
第1组	97.33	84.67	66.67	62.00
第2组	98.67	88.67	68.00	61.33
第3组	100.00	88.67	79.33	64.67
第4组	96.67	84.00	74.00	62.00
第5组	98.00	85.33	88.00	72.67
平均值	98.13	82.27	75.20	64.53

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