轧辊在磨削过程中，轧辊表面会产生振纹等局部缺陷，严重影响带钢表面质量，有效识别轧辊缺陷尺寸大小仍是目前轧机诊断存在的难题之一。利用Matlab小波工具箱构造与信号对应的自适应小波，通过与Haar小波、Morlet小波和试验数据的对比分析，不仅证明了构建自适应小波方法的正确性，而且也验证了自适应小波对轧辊缺陷识别的有效性，为有效识别轧辊缺陷以及分析缺陷尺寸提供了理论基础和实践支撑。
Abstract
Local defects are prone to produce on the surface of roll in grinding process, which seriously affect the quality of the steel strip. Identifications of the defects and the sizes of defects are still existing difficult problems. MATLAB wavelet toolbox is used to generate the adaptive wavelet. Agreement between the adaptive wavelet and the Haar, Morlet wavelet and the experimental results validates the effectiveness of the generated adaptive wavelet method. The results not only validate the effectiveness of identifying the defects, but also provide a theoretical support for identifying defects on the surface of roll.
关键词
轧辊缺陷 /
自适应小波 /
识别
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] Li Hongchun. et al. Analysis of premature failure of work rolls in a cold strip plant[J].Wear. 2007(263).1442-1446
[2] Wu Qiong. et al. Analysis of surface and sub-surface initiated spalling of forged cold work rolls. Engineering Failure Analysis. 2008(15).401-410
[3]魏立新，高江曼，麻诚等. 冷轧平整机工作辊表面粗糙度衰减模型[J]. 钢铁，2015，50(6): 51-56.
Wei Lixin，Gao Jiangman，Ma Cheng. et al. Attenuation model for working roll’s surface roughness in cold rolling temper mill[J]. Iron and Steel，2015, 50(6). 51-56.
[4] Wu S, Wang L, Shao Y, et al. Vibration characteristic analysis of twenty-high rolling mill with local defect on roll surface based on the time-varying contact stiffness[J]. Engineering Failure Analysis, 2014, 42(5). 297-310.
[5] 阳子婧, 蔡力钢, 高立新. 自适应冗余提升小波降噪分析及轴承故障识别应用[J]. 振动与冲击. 2013.32(7).54-57.
Yang Zijing, Cai Ligang, Gao Lixin. Adaptive redundant lifting wavelet denoising analysis and its application in bearing fault identification [J]. Journal of Vibration and Shock. 2013.32(7).54-57.
[6] Chen H.X, Chua Patrick S.K, Lim G.H. Adaptive wavelet transform for vibration signal modeling and application in fault diagnosis of water hydraulic motor [J]. Mechanical Systems and Signal Process. 2006,20,2022-2045.
[7] 刘文艺, 汤宝平, 蒋永华. 一种自适应小波降噪方法[J]. 振动、测试与诊断. 2011.31(1).74-77.
Liu Wenyi, Tang Baoping, Jiang Yonghua. Research on an adaptive wavelet denoising method [J]. Journal of Vibration, Measurement & Diagnosis. 2011.31(1).74-77.
[8] Belsak Ales, Flasker Joze. Determining cracks in gears using adaptive wavelet transform approach [J]. Engineering Failure Analysis. 2010.17.664-671.
[9] Jena D.P, Panigrahi S.N. Bearing and gear fault diagnosis using adaptive wavelet transform of vibration signals [J]. Procedia Engineering. 2012.50.265-274.
[10]辛玉忠, 刘常凯. 判断与衡量两个信号相似性的内积表达式[J]. 潍坊学院学报, 2002, 2(4): 11-12.
Xin Dezhong, Liu Changkai. Inherenet product posture about judging two signals resemble quality[J]. Journal of Weifang University. 2002, 2(4): 11-12.
[11]褚福磊，彭志科，冯志鹏，李志农. 机械故障诊断的现代信号处理方法[M]. 科学出版社. 2009.34-36.
Chu Fulei, Peng Zhike, Feng Zhipeng, Li Zhinong. Modern signal process method in mechanical fault diagnosis [M]. Science Press. 2009.34-36.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
PDF(1257 KB)
