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多传感器数据融合的风电齿轮箱性能衰退评估 预览

Performance degradation assessment of wind turbine generator gearbox based on multi-sensor information fusion
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摘要 针对风电齿轮箱传动结构复杂、所处工况恶劣,难以提取有效振动信号特征进行性能衰退分析的问题,提出多传感器数据融合的风电齿轮箱性能衰退评估方法。该方法将自适应完全集合经验模态分解(CEEM-DAN)、核主分量分析(KPCA)和Hotelling T2统计量相结合,先对风电齿轮箱全寿命的非线性、非平稳振动信号进行CEEMDAN-KPCA降噪处理,再利用KPCA对降噪后的多组振动信号进行融合分析,提取连续的T2值(C-T2)及其时域特征作为评估指标,建立风电齿轮箱性能衰退模型。实验结果表明,该方法对风电齿轮箱振动信号降噪效果显著,C-T2特征有效解决了多组振动信号特征维数膨胀问题,且C-T2时域特征模型比振动信号时域特征模型能更准确地评估风电齿轮箱性能的衰退过程。 Aiming at the problem that wind turbine generator gearbox was difficult to extract the effective features of vibration signals for performance degradation analysis caused by the complex power driving structure and terrible working conditions,a method based on multi-sensor information fusion was proposed.Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN),Kernel Principal Component Analysis(KPCA)and Hotelling’s T-squared statistic(T2)were used to achieve the assessment of performance degradation.A CEEMDAN-KPCA based method was applied to denoise and reconstruct the nonlinear and unstable vibration signals of the life cycle;the reconstructed signals were fused with KPCA,and then continuous T2(C-T2)and related time domain features were extracted to establish the performance degradation models.Experimental results showed that the proposed method had a remarkable effect on denoising the vibration signals,and C-T2 was effective to solve the expansion of feature dimension caused by multiple sets of vibration signals.As well as,models of related time domain features by C-T2 could evaluate the performance degradation more accurate than the time-domain features of vibration signal.
作者 马越 陈捷 洪荣晶 潘裕斌 MA Yue;CHEN Jie;HONG Rongjing;PAN Yubin(College of Mechanical and Power Engineering,Nanjing Tech University,Nanjing 211800,China)
出处 《计算机集成制造系统》 EI CSCD 北大核心 2019年第2期318-325,共8页 Computer Integrated Manufacturing Systems
基金 国家自然科学基金资助项目(51375222) 2014年度高校'青蓝工程'中青年学术带头人资助项目 江苏省科技成果转化专项资金资助项目(BA2012031) 江苏省研究生科研与实践创新计划资助项目(KYCX17_0937).
关键词 风电齿轮箱 多传感器数据融合 信号降噪 性能衰退评估 故障诊断 wind turbine gearbox multi-sensor information fusion signal denoising performance degradation assessment fault diagnosis
作者简介 马越(1993-),男,江苏扬州人,硕士研究生,研究方向:机械设备检测与故障诊断技术,E-mail:1298224684@qq.com;通信作者:陈捷(1971-),女,云南保山人,教授,博士,硕士生导师,研究方向:测试与控制理论、设备寿命检测与健康监测,E-mail:njtechchj@163.com;洪荣晶(1963-),男,福建龙岩人,教授,博士,博士生导师,研究方向:先进数控理论、设备健康监测;潘裕斌(1992-),男,江苏常州人,博士研究生,研究方向:机械设备检测、故障诊断技术。
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