Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (4): 941-953.doi: 10.13229/j.cnki.jdxbgxb.20211226

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Research progress and development trend of extrapolation method in electromechanical equipment load spectrum

Li-juan YU1,2(),Yang AN1,2,Jia-long HE1,2(),Guo-fa LI1,2,Sheng-xu WANG1,2   

  1. 1.Key Laboratory of CNC Equipment Reliability，Ministry of Education，Jilin University，Changchun 130022，China
    2.School of Mechanical and Aerospace Engineering，Jilin University，Changchun 130022，China
  • Received:2021-09-10 Online:2023-04-01 Published:2023-04-20
  • Contact: Jia-long HE E-mail:tallyu@163.com;hejl@jlu.edu.cn

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Abstract:

On the basis of combing and analyzing the latest research results of the extrapolation of the load spectrum of electromechanical equipment at home and abroad， this paper summarizes the research progress and comparative analysis of load extrapolation key technologies： time-domain load extrapolation， rainflow load extrapolation （parametric rainflow， non-parametric rainflow， and rainflow matrix extrapolations）， and quantile extrapolation， in view of the load form and load characteristics of the electromechanical equipment during operation. In the context of the era of big data， the potential direction and development trend of the load spectrum extrapolation method in electromechanical equipment are discussed.

Key words: mechanical engineering, electromechanical equipment, load spectrum, load extrapolation, time domain load extrapolation, rainflow load extrapolation

CLC Number: 

  • TB114.3

Fig.1

Key technologies and process of loadspectrumcompilation"

Fig.2

Statistical chart of number of papers related to load spectrum"

Fig.3

Classification of extrapolation methods in load spectrum compilation"

Fig.4

Sampling principle of BMM，POT and MISmodel"

Fig.5

Extreme value extrapolation method based onBMM-GEV"

Fig.6

Extrapolation method based on GRA-POTmodel"

Fig.7

Fitting results with different value of M"

Fig.8

Flowchart of non-parametric rain-flow extrapolation method based on load extension"

Fig.9

Flow chart of quantile extrapolation optimization"

Table 1

Comparison of extrapolation methods"

方 法优 点缺 点适用范围
基于BMM模型的时域外推在样本容量较大时，无需选择阈值，使用简单方便会忽略部分有效极值点适用于载荷具有周期性质的场合，例如液压泵［32］、数控机床恒速切削状态［20］、风力机［39］等
基于POT模型的时域外推在样本容量比较小时，可完成稳定性高的要求对于合适的阈值选择困难适用于载荷相对平稳的场合，例如液压泵［3］、数控机床恒速切削状态［1］、风力机［46］、伺服刀架［47］等
基于MIS模型的时域外推估计结果对阈值的敏感度低，结果稳定小样本时，较POT模型极值点选取得少，阈值选择困难适用于载荷相对平稳的场合，常用于对风荷载［29，51］、车辆荷载［13］进行极值估计等
参数雨流外推同时考虑了样本均幅值的影响，适用于信号平稳、雨流矩阵形状简单情况依赖于样本数据的分布，在选择参数估计方法时存在主观性适用于平稳随机历程，雨流矩阵较为简单，其中可以用混合分布拟合复杂呈多峰分布的载荷，例如汽车［14，53］、数控机床［16?18］等
非参数雨流外推无需假设样本数据服从某种分布，可突破对母体分布的依赖需要大量的样本数据，核函数和带宽的选择对结果有影响适用于随机性高，雨流矩阵形状复杂的载荷，例如工程机械［10，61，62］、高速列车［5，8］等
雨流矩阵外推能预测短期载荷时间历程内没有出现的大载荷，能完成载荷幅值和频次上的双向外推在利用穿级计数法时上、下阈值的选择存在难度；计算程序相对繁琐适用于需要预测对疲劳损伤贡献大的载荷的场合，例如工程机械［37，63］、高速列车［6，64］等
分位点外推可综合考虑不同工况的载荷差异需要与其他载荷外推方法联合使用适用于需要考虑载荷差异性的场合，例如工程机械［13］、高速列车［6］等
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