Online Degradation Feature Extraction of Shore Bridge Gearbox based on Morphological Fractal and Sliding Window Weibull Fitting

Dejian Sun; Xiong Hu; Bing Wang; Wei Wang; Jichang Lin

doi:10.16578/j.issn.1004.2539.2019.09.026

您当前的位置：

首页 >

文章列表页 >

Online Degradation Feature Extraction of Shore Bridge Gearbox based on Morphological Fractal and Sliding Window Weibull Fitting

Development·Application | 更新时间：2022-09-24

- Online Degradation Feature Extraction of Shore Bridge Gearbox based on Morphological Fractal and Sliding Window Weibull Fitting
- Journal of Mechanical Transmission Vol. 43, Issue 9, Pages: 148-153(2019)
- 作者机构：
  
  1.上海海事大学物流工程学院，上海 201306
  2.中国人民解放军32145部队，河南新乡 453000
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2019.09.026
  CLC：
扫描看全文
孙德建,胡雄,王冰等.基于形态分形与滑动窗威布尔拟合的岸桥减速箱退化特征在线提取[J].机械传动,2019,43(09):148-153.

Sun Dejian,Hu Xiong,Wang Bing,et al.Online Degradation Feature Extraction of Shore Bridge Gearbox based on Morphological Fractal and Sliding Window Weibull Fitting[J].Journal of Mechanical Transmission,2019,43(09):148-153.
孙德建,胡雄,王冰等.基于形态分形与滑动窗威布尔拟合的岸桥减速箱退化特征在线提取[J].机械传动,2019,43(09):148-153. DOI： 10.16578/j.issn.1004.2539.2019.09.026.

Sun Dejian,Hu Xiong,Wang Bing,et al.Online Degradation Feature Extraction of Shore Bridge Gearbox based on Morphological Fractal and Sliding Window Weibull Fitting[J].Journal of Mechanical Transmission,2019,43(09):148-153. DOI： 10.16578/j.issn.1004.2539.2019.09.026.

摘要

针对起升机构齿轮箱的退化特征提取问题，提出一种基于数学形态分形维数与滑动窗威布尔拟合的退化特征在线提取方法。首先，按照分析周期计算振动能量谱的形态分形维数，形成分形演化曲线；设置滑动窗口宽度与步长，对窗口内的分形序列进行三参数威布尔拟合，以模型的尺度参数作为性能退化特征指标；采用工业现场监测的起升机构齿轮箱全寿命数据作为基础，验证了该方法的有效性。结果表明，数学形态分形维数能够刻画振动能量谱的分形复杂度，威布尔分布的尺度参数能够平滑地反映分形曲线的性能退化趋势，为进一步解决在线健康状态评估问题奠定理论方法基础。

Abstract

In order to solve the degenerative feature extraction issue of hoisting mechanism gearbox, an online extraction method for degeneration feature based on mathematical morphological fractal dimension and sliding window Weibull fitting is proposed. Firstly, according to the analysis period, the fractal dimension of the vibration energy spectrum is calculated and forming a fractal evolution curve. Performing a three-parameter Weibull fitting on the fractal sequence in the window after setting the sliding window width and step size and the scale parameter of the model is used as the performance degradation feature indicator. The effectiveness of the method is verified by using the full-life data of the hoisting gearbox of the industrial site monitoring. The results show that the mathematical morphological fractal dimension is able to describe the fractal complexity of vibration energy spectrum. The scale parameter of Weibull distribution is able to reflect the performance degradation trend of fractal curve smoothly, which lays a theoretical foundation for further solving the problem of online health assessment.

关键词

岸桥退化特征提取分形维数威布尔分布齿轮箱

Keywords

Shore bridgeDegradation feature extractionFractal dimensionWeibull distributionGearbox

references

RUGGERI P，ESTHER FRUZZETTI V M，SCARPELLI G.Renovation of quay walls to meet more demanding requirements：Italian experiences［J］.Coastal Engineering，2019，147（5）：25-33.

ARANEO R，ATTOLINI G，CELOZZI S，et al.Time-domain shielding performance of enclosures：a comparison of different global approaches［J］.IEEE Transactions on Electromagnetic Compatibility，2016，58（2）：434-441.

RANDALL R B，ANTONI J.Rolling element bearing diagnostics-atutorial［J］.Mechanical Systems & Signal Processing，2011，25（2）：485-520.

GAO Y，VILLECCO F，LI M，et al.Multi-scale permutation entropy based on improved LMD and HMM for rolling bearing diagnosis［J］.Entropy，2017，19（4）：176-182.

吴文轩，王志坚，张纪平，等.基于峭度的VMD分解中k值的确定方法研究［J］.机械传动，2018，42（8）：153-157.

范国良，李爱平，刘雪梅，等.基于信息熵与Lempel-Ziv的拧紧设备性能评估方法［J］.振动、测试与诊断，2019（1）：88-94.

郑近德，潘海洋，戚晓利，等.复合层次模糊熵及其在滚动轴承故障诊断中的应用［J］.中国机械工程，2016，27（15）：2048-2055.

杨大为，冯辅周，赵永东，等.VMD样本熵特征提取方法及其在行星变速箱故障诊断中的应用［J］.振动与冲击，2018，37（16）：198-205.

WANG B，HU X，LI H R.Rolling Bearing performance degradation condition recognition based on mathematical morphological fractal dimension and fuzzy C-means［J］.Measurement，2017，109：1-8.

康守强，叶立强，王玉静，等.基于数学形态学和IFOA-SVR的滚动轴承可靠度预测方法［J］.机械工程学报，2017，53（8）：201-208.

LEE J，QIU H，YU G，et al.Bearing dataset from IMS of university of Cincinnati and NASA ames prognostics data repository［EB/OL］.［2010-12-10］.http：//ti.arc.nasa.gov/tech/dashpcoe/prognostic-data-repositoryhttp://ti.arc.nasa.gov/tech/dashpcoe/prognostic-data-repository.

NECTOUX P，GOURIVEAU R，MEDJAHER K，et al.PRONOSTIA：an experimental platform for bearings accelerated degradation tests［C］.IEEE International Conference on Prognostics and Health Management.New York：IEEE，2012：1-8.

陈征，温广瑞，张志芬，等.集成轴心轨迹信息熵及盒维数的转子故障程度判别与故障预示［J］.西安交通大学学报，2019，53（2）：40-46.

何志坚，周志雄，黄向明.基于变分模态分解的关联维数及相关向量机的刀具磨损状态监测［J］.计量学报，2018，39（2）：182-186.

王冰，李洪儒，陈强华，等.基于数学形态分形维数与模糊C均值聚类的滚动轴承退化状态识别［J］.兵工学报，2015，36（10）：1982-1990.

陈昌，汤宝平，吕中亮.基于威布尔分布及最小二乘支持向量机的滚动轴承退化趋势预测［J］.振动与冲击，2014，33（20）：52-56.

王奉涛，陈旭涛，柳晨曦，等.基于KPCA和WPHM的滚动轴承可靠性评估与寿命预测［J］.振动、测试与诊断，2017，37（3）：476-483.

唐刚，李建霞，胡雄.准确近似聚类算法及其在岸桥拉杆状态监测中的应用［J］.东华大学学报（自然科学版），2018，44（4）：590-594.

CHAUDHURI B B，SARKAR N.Texture segmentation using fractal dimension［J］.IEEE Transactions on Pattern Analysis and Machine Intelligence，1995，17（1）：72-77.

MARAGOS P，SUN F K.Measuring the fractal dimension of signals：morphological covers and iterative optimization［J］.IEEE Transactions on Signal Processing，1993，41（1）：108-121.

刘志川，唐力伟，曹立军.基于卡尔曼滤波和数学形态学分形分析的齿轮箱故障诊断［J］.机械传动，2014，38（7）：141-144.

郑锐.三参数威布尔分布参数估计及在可靠性分析中的应用［J］.振动与冲击，2015，34（5）：78-81.

顾蓓青，徐晓岭，王蓉华.两参数对数正态分布与威布尔分布的近似极大似然估计［J］.统计与决策，2019，35（3）：34-39.

张瑞亮，颜天晓，王铁，等.基于多重分形谱和灰色模型GM（1，1）的齿轮接触疲劳寿命预测［J］.机械传动，2017，41（7）：97-101.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Study of Internal Pressure Field Characteristics in Gearboxes of a Heavy-haul Locomotive Based on the Lattice Boltzmann Method

Effect of Time-varying Friction Factors on the Contact Fatigue Life of Gears

Gearbox Fault Diagnosis Based on the LMD Cloud Model and PSO-KELM

Study on Scuffing Test and Data Management of the 18CrNiMo7-6 Gear-4106 Aviation Oil Tribology System

Research on Gearbox Impact Feature Extraction Method Based on the Improved ESMD and Dynamics Model

Related Author

No data

Related Institution

Research Institute of Technology Center, CRRC Datong Co., Ltd.

State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University

Zhengzhou Research Institute of Mechanical Engineering Co., Ltd.

Shaanxi Fast Gear Co., Ltd.

ZRIME Gearing Technology Co., Ltd.

⁰