[1]鄢小安,贾民平.自适应多尺度开闭平均-hat变换及在轴承故障诊断中的应用[J].东南大学学报(自然科学版),2019,49(5):826-832.[doi:10.3969/j.issn.1001-0505.2019.05.003]
 Yan Xiaoan,Jia Minping.Adaptive multi-scale opening and closing average-hat transform and its application in bearing fault diagnosis[J].Journal of Southeast University (Natural Science Edition),2019,49(5):826-832.[doi:10.3969/j.issn.1001-0505.2019.05.003]
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自适应多尺度开闭平均-hat变换及在轴承故障诊断中的应用()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
49
期数:
2019年第5期
页码:
826-832
栏目:
机械工程
出版日期:
2019-09-20

文章信息/Info

Title:
Adaptive multi-scale opening and closing average-hat transform and its application in bearing fault diagnosis
作者:
鄢小安12贾民平1
1东南大学机械工程学院, 南京 211189; 2南京林业大学机械电子工程学院, 南京 210037
Author(s):
Yan Xiaoan12 Jia Minping1
1School of Mechanical Engineering, Southeast University, Nanjing 211189, China
2School of Mechatronics Engineering, Nanjing Forestry University, Nanjing 210037, China
关键词:
多尺度开闭平均-hat变换 谱峭度 布谷鸟优化算法 轴承故障诊断
Keywords:
multi-scale opening and closing average-hat transform(MAVGH) spectral kurtosis cuckoo search algorithm bearing fault diagnosis
分类号:
TH17
DOI:
10.3969/j.issn.1001-0505.2019.05.003
摘要:
针对传统多尺度形态学分析方法(TMMA)因采用全部尺度的算术平均值作为输出结果而影响故障特征提取的问题,提出了一种自适应多尺度开闭平均-hat变换方法,并将其成功应用于轴承故障诊断中.首先,借助单尺度形态学算子、多尺度结构元素和加权运算等手段,构建一种多尺度开闭平均-hat变换(MAVGH);随后,通过谱峭度指标确定MAVGH的最优尺度范围,并运用布谷鸟优化算法自适应搜索最优尺度范围内的组合权重系数.仿真和工程应用分析结果表明,对于仿真信号,相比TMMA、WMMG、EMD和小波分析,提出方法的特征频率强度系数(CFIC)分别提高了35.88%、33.91%、31.13%和46.97%;对于应用实例,相比TMMA、WMMG、EMD和小波分析,提出方法的CFIC值分别提高了6.26%、8.06%、2.84%和7.68%.
Abstract:
In traditional multi-scale morphological analysis(TMMA), an arithmetic mean of all scales was regarded as the output results, influencing fault feature extraction results. To solve this problem, a method called the adaptive multi-scale opening and closing average-hat transformation, was proposed and successfully applied in the bearing fault detection. Firstly, by means of single scale morphological operator, a multi-scale structuring elements and weighting operation, a multi-scale opening and closing average-hat transformation(MAVGH)was presented. Then, the optimal scale range of MAVGH was determined by the spectral kurtosis index, and cuckoo search algorithm(CSA)was devoted to searching adaptively combination weight coefficients within the optimal scale range. Simulation and engineering application analysis results indicate that the characteristic frequency intensity coefficient(CFIC)of the method for simulation signal can be improved, respectively, by 35.88%, 33.91%, 31.13% and 46.97% compared with TMMA, WMMG, EMD and wavelet analysis, while CFIC of the proposed method for application example can be improved, respectively, by 6.26%, 8.06%, 2.84% and 7.68% compared with TMMA, WMMG, EMD and wavelet analysis.

参考文献/References:

[1] 钟秉林, 黄仁, 贾民平.机械故障诊断学[M].北京: 机械工业出版社, 2013:1-410.
[2] Hu A J, Xiang L. Selection principle of mathematical morphological operators in vibration signal processing[J]. Journal of Vibration and Control, 2016, 22(14): 3157-3168. DOI:10.1177/1077546314560783.
[3] Li Y F, Zuo M J, Lin J H, et al. Fault detection method for railway wheel flat using an adaptive multiscale morphological filter[J]. Mechanical Systems and Signal Processing, 2017, 84: 642-658. DOI:10.1016/j.ymssp.2016.07.009.
[4] Zhang L J, Xu J W, Yang J H, et al. Multiscale morphology analysis and its application to fault diagnosis[J]. Mechanical Systems and Signal Processing, 2008, 22(3): 597-610. DOI:10.1016/j.ymssp.2007.09.010.
[5] Shen C Q, He Q B, Kong F R, et al. A fast and adaptive varying-scale morphological analysis method for rolling element bearing fault diagnosis[J]. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2013, 227(6): 1362-1370. DOI:10.1177/0954406212460628.
[6] Li B, Zhang P L, Wang Z J, et al. Gear fault detection using multi-scale morphological filters[J]. Measurement, 2011, 44(10): 2078-2089. DOI:10.1016/j.measurement.2011.08.010.
[7] Chen Q, Chen Z W, Sun W, et al. A new structuring element for multi-scale morphology analysis and its application in rolling element bearing fault diagnosis[J]. Journal of Vibration and Control, 2015, 21(4): 765-789. DOI:10.1177/1077546313486163.
[8] Li B, Zhang P L, Wang Z J, et al. A weighted multi-scale morphological gradient filter for rolling element bearing fault detection[J]. ISA Transactions, 2011, 50(4): 599-608. DOI:10.1016/j.isatra.2011.06.003.
[9] 鄢小安, 贾民平. 参数优化的组合形态-hat变换及其在风力发电机组故障诊断中的应用[J].机械工程学报,2016,52(13):103-110.DOI:10.3901/JME.2016.13.103.
Yan X A, Jia M P. Parameter optimized combination morphological filter-hat transform and its application in fault diagnosis of wind turbine[J]. Journal of Mechanical Engineering, 2016, 52(13): 103-110. DOI:10.3901/JME.2016.13.103. (in Chinese)
[10] Jalba A C,Wilkinson M H F, Roerdink J B T M. Morphological hat-transform scale spaces and their use in pattern classification[J].Pattern Recognition,2004,37(5):901-915.DOI:10.1016/j.patcog.2003.09.009.
[11] Raj A S,Murali N. Early classification of bearing faults using morphological operators and fuzzy inference[J].IEEE Transactions on Industrial Electronics,2013,60(2):567-574.DOI:10.1109/tie.2012.2188259.
[12] Dong Y B, Liao M F, Zhang X L, et al. Faults diagnosis of rolling element bearings based on modified morphological method[J]. Mechanical Systems and Signal Processing, 2011, 25(4): 1276-1286. DOI:10.1016/j.ymssp.2010.10.008.
[13] Yan X A, Jia M P, Zhang W, et al. Fault diagnosis of rolling element bearing using a new optimal scale morphology analysis method[J].ISA Transactions,2018,73:165-180.DOI:10.1016/j.isatra.2018.01.004.
[14] Antoni J. The spectral kurtosis: A useful tool for characterising non-stationary signals[J]. Mechanical Systems and Signal Processing, 2006, 20(2): 282-307. DOI:10.1016/j.ymssp.2004.09.001.
[15] Yang X S,Deb S.Cuckoo search: Recent advances and applications[J].Neural Computing and Applications,2014,24(1):169-174.DOI:10.1007/s00521-013-1367-1.

备注/Memo

备注/Memo:
收稿日期: 2018-12-30.
作者简介: 鄢小安(1989—),男,博士生;贾民平(联系人),男,博士,教授,博士生导师,mpjia@seu.edu.cn.
基金项目: 国家自然科学基金资助项目(51675098)、江苏省研究生科研创新计划资助项目(KYXC17_0059).
引用本文: 鄢小安,贾民平.自适应多尺度开闭平均-hat变换及在轴承故障诊断中的应用[J].东南大学学报(自然科学版),2019,49(5):826-832. DOI:10.3969/j.issn.1001-0505.2019.05.003.
更新日期/Last Update: 2019-09-20