On‐line reliability estimation for individual components using statistical degradation signal models
On‐line reliability estimation for individual components using statistical degradation signal models
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DOI：
10.1002/qre.453
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发表时间：
2002
影响因子：
2.3
通讯作者：
R. Chinnam
中科院分区：
文献类型：
--
作者：
R. Chinnam
除了极少数例外，大多数当代可靠性工程方法都是针对估计系统、子系统或组件的总体特性。这样提取的信息对于制造商和其他处理相对大量产品的人来说是非常有价值的。相反，最终用户通常对系统中使用的“特定”组件的行为更感兴趣，以达到优化的组件替换或维护策略，从而提高系统利用率，同时降低风险和维护成本。解决这一需求的传统方法是通过退化信号来监控组件，并将组件的状态“分类”为离散的类别，例如“好”，“坏”和“介于两者之间”的类别。在这种情况下，人们可以开发有效的退化信号预测模型，并在退化信号空间中精确定义部件故障，然后，人们可以超越分类方法，为单个单元提供更有力的可靠性估计和预测方案。本文用“一般”多项式回归模型对退化信号建模证明了这种方法的可行性。所提出的方法允许残差中的一阶自相关以及加权回归。参数自举技术用于计算估计可靠性的置信区间。针对一个刀具监控问题对该方法进行了评价。特别是，该方法用于监测用于在不锈钢金属板上钻孔的高速钢钻头。第二项研究涉及对文献中的疲劳裂纹扩展数据进行建模和预测。这项任务包括估计和预测由于疲劳-裂纹-扩展而预期失效的板的可靠性。两项研究都揭示了非常有希望的结果。版权所有©2002约翰威利父子有限公司
With very few exceptions, most contemporary reliability engineering methods are geared towards estimating a population characteristic(s) of a system, subsystem or component. The information so extracted is extremely valuable for manufacturers and others that deal with product in relatively large volumes. In contrast, end users are typically more interested in the behavior of a ‘particular’ component used in their system to arrive at optimal component replacement or maintenance strategies leading to improved system utilization, while reducing risk and maintenance costs. The traditional approach to addressing this need is to monitor the component through degradation signals and ‘classifying’ the state of a component into discrete classes, say ‘good’, ‘bad’ and ‘in‐between’ categories. In the event, one can develop effective degradation signal forecasting models and precisely define component failure in the degradation signal space, then, one can move beyond the classification approach to a more vigorous reliability estimation and forecasting scheme for the individual unit. This paper demonstrates the feasibility of such an approach using ‘general’ polynomial regression models for degradation signal modeling. The proposed methods allow first‐order autocorrelation in the residuals as well as weighted regression. Parametric bootstrap techniques are used for calculating confidence intervals for the estimated reliability. The proposed method is evaluated on a cutting tool monitoring problem. In particular, the method is used to monitor high‐speed steel drill‐bits used for drilling holes in stainless‐steel metal plates. A second study involves modeling and forecasting fatigue‐crack‐growth data from the literature. The task involved estimating and forecasting the reliability of plates expected to fail due to fatigue‐crack‐growth. Both studies reveal very promising results. Copyright © 2002 John Wiley & Sons, Ltd.