锻压技术

基于EEMD-DWT方法的冷轧机架振动信号联合降噪研究

英文标题：Joint noise reduction research on vibration signal for cold rolling mill frame based on EEMD-DWT method

作者：康家林1 2 徐江莉1 姚传安2

分类号：TH206

出版年,卷(期):页码：2023,48(7):156-161

摘要：

为了提高冷轧机的故障诊断能力，综合运用集合经验模态分解（EEMD）算法和小波阈值变换（DWT）的降噪技术，设计了一种EEMDDWT联合降噪技术，确保在去除噪声的前提下充分保留有用特征。采用EEMDDWT模式去噪时获得了光滑的曲线信号，表现出优异的去噪效果。应用信号结果表明:以EEMDDWT去噪时包含明显的冲击特征，有效地减小了位于幅值接近零区域的噪声分量，确保噪声被充分去除的同时实现原有振动特征的有效保留。单独利用DWT或EEMD方法去噪时将会引起有效信息大量丢失，以EEMDDWT联合去噪方法进行处理时能够达到理想的降噪效果，充分保留信号包含的有用参数。其能够准确识别冷轧机不同的故障程度，对提高同类机械传动设备的故障诊断水平具有很好的理论支撑意义。

In order to improve the fault diagnosis ability of cold rolling mill, comprehensively using the noise reduction technology of Ensemble Empirical Mode Decomposition (EEMD) algorithm and Discrete Wavelet Transformation(DWT), a joint noise reduction technology by EEMD-DWT was designed, and it was ensured that the useful features were fully preserved under the premise of removing noise. Then, the smooth curve signal was obtained by using EEMD-DWT mode for denoising, which showed excellent denoising effect. The application signal results show that when denoising with EEMD-DWT, it includes obvious impact characteristics, which effectively reduces the noise component in the region where the amplitude is near zero, and ensures that the original vibration characteristics are effectively reserved when the noise is fully removed. When DWT or EEMD method is used alone for denoising, a large amount of effective information is lost. When the EEMD-DWT joint noise reduction method is used for processing, the ideal noise reduction effect is achieved, and the useful parameters contained in the signal are fully preserved. Thus, this research can accurately identify different fault degrees of cold rolling mill, which has good theoretical support significance for improving the fault diagnosis level of similar mechanical transmission equipment.

基金项目：

河南省高等学校重点科研项目（21B460003）

作者简介：康家林（1988-），男，硕士，讲师 E-mail：xjl6323@163.com

参考文献：

[1]胡璇, 李春, 叶柯华, 等. 改进灰狼算法优化支持向量机在风力机冷轧机故障诊断中的应用[J]. 机械强度, 2021, 43(6): 1289-1296.

Hu X, Li C, Ye K H, et al. Application of improved grey wolf algorithm optimized support vector machine in wind turbine gearbox fault diagnosis [J]. Journal of Mechanical Strength, 2021, 43(6): 1289-1296.

[2]陶九志, 黄润华, 马卫平. 基于IHPS/ISPS滤波和Hilbert变换解调的齿轮断齿故障诊断[J]. 机械强度, 2021, 43(6): 1303-1308.

Tao J Z, Huang R H, Ma W P. Fault diagnosis of gear tooth breaking based on IHPS/ISPS filter and hilbert transform demodulation [J]. Journal of Mechanical Strength, 2021, 43(6): 1303-1308.

[3]杨春晖, 徐霆. 振动监测技术在风电齿轮箱解体检修中的应用[J]. 上海电力大学学报, 2021, 37(S1): 57-60,63.

Yang C H, Xu T. Application of vibration monitoring technology in wind turbine gearbox disassembly and maintenance [J]. Journal of Shanghai University of Electric Power, 2021, 37(S1): 57-60,63.

[4]马芸婷, 张超, 王宇晨. PCASAE的齿轮箱故障诊断方法研究[J]. 机械设计与制造，2022,（3）：144-147,152.

Ma Y T, Zhang C, Wang Y C. Research on PCASAE gearbox fault diagnosis method [J]. Machinery Design & Manufacture，2022,（3）：144-147,152.

[5]秦辞海, 赵睿智, 王月强, 等. 基于LSGAN和VMDMPEKELM的风机齿轮箱故障诊断[J]. 机械传动, 2021, 45(11): 153-160.

Qin C H, Zhao R Z, Wang Y Q, et al. Fault diagnosis of fan gearbox based on LSGAN and VMDMPEKELM [J]. Journal of Mechanical Transmission, 2021, 45(11): 153-160.

[6]何雷, 刘溯奇. EMDAR谱分析和SVM的变速箱故障诊断[J]. 机械设计与制造, 2021(11): 56-59,64.

He L, Liu S Q. Gearbox fault diagnosis based on EMDAR spectrum analysis and SVM [J]. Machinery Design & Manufacture, 2021(11): 56-59,64.

[7]樊家伟, 郭瑜, 伍星, 等. 基于LSTM神经网络和故障特征增强的行星齿轮箱故障诊断[J]. 振动与冲击, 2021, 40(20): 271-277.

Fan J W, Guo Y, Wu X, et al. Fault diagnosis of planetary gearboxes based on LSTM neural network and fault feature enhancement [J]. Journal of Vibration and Shock, 2021, 40(20): 271-277.

[8]徐晋宏, 魏秀业, 贺妍, 等. 基于CEEMDAN样本熵与PNN的行星齿轮故障诊断[J]. 冷轧机与液压, 2021, 49(20): 179-183.

Xu J H, Wei X Y, He Y, et al. Fault diagnosis of planetary gear based on CEEMDAN sample entropy and PNN [J]. Machine Tool & Hydraulics, 2021, 49(20): 179-183.

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