Design and Analysis of Experiment
Design and Analysis of Experiment
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DOI：
10.1080/00401706.2000.10486005
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发表时间：
2000-05
期刊：
影响因子：
2.5
通讯作者：
J. V. Grice
中科院分区：
文献类型：
--
作者：
J. V. Grice
建议结合使用统计分布的设计方法和策略，并着眼于支持技术所起的作用，如故障模式和影响分析(FMEA)、故障树分析(FTA)以及设计审查和审计。无论是从设计的角度还是从现场性能的角度来看，A.D.S.卡特显然在机械可靠性方面都有着丰富的实践经验和知识。这本书的优点在于它描述了机械失效模式，并基于载荷/强度干涉模型、S-N曲线、裂纹扩展扩展模型和Miner规则等工具开发了设计指南。标准的可靠性教科书通常忽略这些主题，或者只草率地对待它们[O‘Connor(1991)确实讨论了许多这样的模型，Rao(1992)也是如此]。卡特对这些主题提供了许多实用的见解，这些见解来自于他亲自参与的文献和案例研究。本书最薄弱的部分发生在作者离开机械工程领域，冒险进入统计建模领域的时候。他对统计分布的讨论不包括基本的可靠性模型，如对数正态分布或极值分布。虽然他主张使用Miner规则，但他没有提到Birnbaum-Saunders分布，该分布可以从基于Miner规则的概率论证中推导出来(Mann，Schafer和SingPurwalla，1974)。除了对中心极限定理的误导性解释外，没有提到使用由失效机制的性质引起的论点来推导统计分布模型。卡特断言，大多数时候，只有少数几个物理现象是我们需要建模的分布的根本原因，因此没有足够的小附加项来引用中心极限论点并证明使用正态分布模型是合理的。这忽略了这样一种可能性，即少数高水平的因果现象本身可以分解成许多小的相加的物理过程。卡特具有经验主义者对统计模型的不信任，特别是在尾部效应方面。他提出了许多反对外推的有趣论点，只有当有可能对尾部事件进行足够的采样，从而有很高的(非参数)信心看到一个存在于给定概率水平的问题时，他才看起来很舒服。然而，他最终得出的结论是，基于统计模型的可靠性设计至少与以前的方法一样好，事实证明，在他有足够数据验证结果的少数情况下，可靠性是令人惊讶的准确。不幸的是，当他建议设计师使用卡方拟合度检验50%显著水平值作为检验标准时，他犯了一个严重的错误。他们可以省去相关的努力，简单地抛硬币，只接受正面，并具有相同的准确性，如果事实上，模型是正确的。卡特还认为，通常倡导的方法，如FMEA和FTA，对关心零配件机械可靠性的设计师来说没有什么价值，而设计审查和审计应该包括在设计过程中。总而言之，阅读卡特的书的设计师不会学到太多关于可靠性统计的知识，但已经知道如何使用统计方法的可靠性工程师将对机械设计的方法和实际问题有一些洞察。
ommends a design methodology and strategy incorporating the use of statistical distributions and looks at the role played by supporting techniques such as failure modes and effects analysis (FMEA), fault tree analysis (FTA), and design reviews and audits. A. D. S. Carter clearly has much practical experience and knowledge concerning mechanical reliability, from both a design point of view and a field performance perspective. The strength of this book lies in its description of mechanical failure modes and its development of design guidelines based on tools such as the load/strength interference model, s-N curves, crack growth propagation models, and Miner’s rule. Standard reliability textbooks typically omit these subjects or treat them only cursorily [O’Connor (1991) did discuss many of these models, as did Rao (1992)]. Carter offers many practical insights into these subjects, derived both from the literature and case studies he was personally involved with. The weakest parts of the book occur when the author leaves the domain of mechanical engineering and ventures onto the turf of statistical modeling. His discussion of statistical distributions does not include basic reliability models such as the lognormal distribution or extreme value distributions. Though he advocates using Miner’s rule, he does not mention the Birnbaum-Saunders distribution, which can be derived from a probabilistic argument based on Miner’s rule (Mann, Schafer, and Singpurwalla 1974). No mention is made of deriving statistical distribution models using arguments arising from the nature of the failure mechanisms other than a misleading interpretation of the central limit theorem. Carter asserts that most of the time only a handful of physical phenomena are the root cause of the distributions we need to model and therefore there are not enough small additive terms to invoke a central limit argument and justify using a normal distribution model. This ignores the possibility that the few highlevel causative phenomena can, themselves, be broken down into many small additive physical processes. Carter has an empiricist’s mistrust of statistical models, especially with respect to tail effects. He offers many interesting arguments against extrapolation and seems comfortable only when it is possible to sample enough tail events to have a high (nonparametric) confidence of seeing a problem if it exists at a given probability level. He does finally conclude, however, that designing for reliability based on statistical models is at least as good as prior methods and turns out to be surprisingly accurate in the few instances in which he had sufficient data to validate results. Unfortunately, he makes a serious error when he advises designers to check their statistical models by using a chi-squared goodness-of-fit test with a 50% significance level value as the test criterion. They could save the effort involved and simply flip a coin and accept only on heads and have the same accuracy if, in fact, the model is correct. Carter also argues that commonly advocated methodologies such as FMEA and FTA offer little of value for the designer concerned with piecepart mechanical reliability, whereas design reviews and audits should be included in the design process. In summary, a designer reading Carter’s book will not learn much about reliability statistics, but a reliability engineer who already knows how to use statistical methods will gain some insight into the methods and practical problems of mechanical design.