Extrapolation of load histories and spectra
Extrapolation of load histories and spectra
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DOI：
10.1111/j.1460-2695.2006.00982.x
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发表时间：
2006-03
期刊：
影响因子：
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通讯作者：
Pär Johannesson
中科院分区：
文献类型：
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作者：
Pär Johannesson
在疲劳寿命评估中，材料特性和载荷特性都是重要的参数。部件的寿命可以通过进行疲劳测试来实验性地确定。为了可靠地预测使用寿命，应使用代表使用载荷的变幅载荷进行测试。本文主要研究将实测载荷历史外推到更长时间段的问题，例如，外推到整个设计寿命。利用统计极值理论，提出了一种新的时间序列外推方法。所得的外推载荷谱进行比较，使用的方法外推雨流矩阵的结果。介绍部件的使用寿命取决于载荷条件和疲劳强度。因此，为了获得适当的疲劳设计，考虑真实的客户载荷非常重要。许多工程方法都是基于寻找最坏的情况，其中最坏的情况通常应被解释为某个严重的客户;参见例如Grubisic [3]，Klatschke & Schutz [5]，Socie [7]。这些程序涉及将测得的载荷外推到更长的时间段，通常是设计寿命。这种外推法应允许比观察到的载荷更极端的载荷。在Socie [7]中，使用了Dressler等人[2]提出的统计方法，其中使用核平滑外推雨流矩阵。本文的主要议题是一种方法，基于统计极值理论，外推的测量时间信号到一个较长的时间段，允许更多的极端周期比最大的观测。该方法可以应用于任何信号和任何目的，例如，外推的时间信号可以是疲劳测试的输入，或FEM疲劳分析的载荷输入。首先将在非常短的信号上解释该方法，其中更容易看到该方法如何工作。然后将其应用于两个负载历史，一个来自火车，一个来自汽车。此外，将从外推时间信号获得的载荷谱与从计算外推雨流矩阵的更直接方法获得的载荷谱进行比较。负载历史的外推方法测量信号通常只代表设计寿命的很短一部分。在进行变幅试验时，通常使用实测载荷历史，并重复该载荷块直至失效。这具有的缺点是，即使其他周期也是可能的，但是仅测量信号中的周期将出现在外推中。特别是，这对于最具破坏性的大振幅周期至关重要。这里的方法将是重复测量的负载区组，但修改每个区组中的最大值和最小值。每个块的随机再生基于
In fatigue life assessments both the material properties and the load characteristics are essential parameters. The life of a component can be experimentally found by performing fatigue tests. In order to get reliable predictions of the life in service, the tests should be performed using variable amplitude loadings that are representative for the service loads. This paper concentrates on the problem of extrapolating a measured load history to a longer time period, for example to a full design life. Using statistical extreme value theory, a new method for extrapolating a time sequence is presented. The obtained extrapolated load spectrum is compared to the result using a method for extrapolating the rainflow matrix. Introduction The service life of a component depends on both the load conditions and the fatigue strength. Hence, in order to get a proper fatigue design it is important to consider real customer loads. Many engineering methods are based on finding the worst case scenario, where worst often should be interpreted as a certain severe customer; see e.g. Grubisic [3], Klatschke & Schutz [5], Socie [7]. These procedures involve extrapolation of measured loads to longer periods of time, typically to a design life. Such an extrapolation should allow more extreme loads than the observed ones. In Socie [7] a statistical method is used that was proposed by Dressler et al. [2], where the rainflow matrix is extrapolated using kernel smoothing. The main topic of this paper is a method, based on statistical extreme value theory, for extrapolation of a measured time signal to a longer time period, allowing for more extreme cycles than the largest observed ones. The method can be applied to any signal and any purpose, e.g. the extrapolated time signal could be the input to a fatigue test, or the load input to a FEM fatigue analysis. The method will first be explained on a very short signal, where it is easier to see how the method works. It will then be applied to two load histories, one from a train, and one from a car. Further, the obtained load spectrum from the extrapolated time signal will be compared to the load spectrum obtained from the more direct method of computing the extrapolated rainflow matrix. Method for Extrapolation of a Load History A measured signal often represents only a very short part of the design life. When performing variable amplitude tests it is customary to use a measured load history, and repeat this load block until failure. This has the drawback that only the cycles in the measured signal will appear in the extrapolation, even though also other cycles are possible. Especially, this can be critical for the most damaging large amplitude cycles. The methodology here will be to repeat the measured load block, but modify the largest maxima and lowest minima in each block. The random regeneration of each block is based on