A set-of-numbers is NOT a data-set

Ascher, Harold

doi:10.1109/24.784271

Cited by 17 publications

(26 citation statements)

References 12 publications

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“…Authors such as Newton (1991) andAscher (1999) have demonstrated how insidious such a practice is, as it leads to terminological and conceptual ambiguities, pessimistic estimates, and contradictory conclusions from the same data set. The literature, however, does not offer many examples on how to avoid common mistakes in empirical data analysis.…”

Section: Assumptions and "Forgotten" Lessons In Field Data Analysismentioning

confidence: 99%

“…(Meeker and Escobar, 1998;Lewis, 1996). Regardless of which distribution is used, this is not appropriate for two main reasons (Ascher, 1999):…”

Section: Exhibit 4 Herementioning

confidence: 99%

“…However, only by providing adequate context regarding the underlying situation from which the data arose does one avoid misconceptions between approaches meant for repairable items and those meant for non-repairable items (Ascher, 1999).…”

Section: Exhibit 1 Here Exhibit 2 Herementioning

confidence: 99%

“…In practice, models may be chosen for their computational tractability rather than for their appropriateness to the situation described by the data being analyzed; terms such as "failure rate" may be used in situations so heterogeneous that they are almost devoid of meaning. A set-of-numbers may be called a "data set" despite lacking information about the specific context in which the numbers were generated (Ascher, 1983a;Ascher, 1999). Hence, to evaluate whether a model is adequate for the purpose at hand, one should first question what the field data represents (Evans, 1995) and be aware that impeccable mathematics is not a guarantee that the statistics are meaningful in decision making (Evans, 1999).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Applying Forgotten Lessons in Field Reliability Data Analysis to Performance-Based Support Contracts

Settanni

Newnes

Thenent

et al. 2016

Engineering Management Journal

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Section: Assumptions and "Forgotten" Lessons In Field Data Analysismentioning

confidence: 99%

“…(Meeker and Escobar, 1998;Lewis, 1996). Regardless of which distribution is used, this is not appropriate for two main reasons (Ascher, 1999):…”

Section: Exhibit 4 Herementioning

confidence: 99%

Section: Exhibit 1 Here Exhibit 2 Herementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Applying Forgotten Lessons in Field Reliability Data Analysis to Performance-Based Support Contracts

Settanni

Newnes

Thenent

et al. 2016

Engineering Management Journal

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“…For example, renewal processes (same-asnew) and non-homogeneous Poisson processes (same-as-old) seem simple in mathematical definitions, but in practice, the assumptions are rarely satisfied, just as Guo and Love [42] reported that both same-as-new and same-as-old are very strong assumptions. Twisted with simplicity seeking, misconceptions and malpractice in reliability engineering literature are persistent symptoms, just as exposed systematically in Ascher and Feingold [1], Ascher [2] and [5], Ascher and Kobbacy [3], Ascher and Hansen [4] and [6]. Therefore, what is a simple model is a very debatable issue.…”

Section: Introductionmentioning

confidence: 99%

Generalized models of repairable systems: A survey via stochastic processes formalism

Guo¹,

Asher²,

Love³

2014

ORiON

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In this article, we survey the developments in the generalised models of repairable systems reliability during 1990s, particularly the last five years. In this field, we notice the sharp fundamental problem that voluminous complex models were developed but there is an absence of sufficient data of interest for justifying the success in tackling the real engineering problems. Instead of following the myth of using simple models to face the complex reality, we select and review some practical models, particularly the stochastic processes behind them. The Models in three quick growth areas: age models, condition monitoring technique related models, say, proportional intensity and their extensions, and shock and wearing models, including the delay-time models are reviewed. With the belief that only those stochastic processes reflecting the instinct nature of the actual physical processes of repairable systems, without excessive assumptions, may have a better chance to meet the demands of engineers and managers.

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Repairable Systems Reliability

Ascher

2007

Encyclopedia of Statistics in Quality and Reliability

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Almost all systems of interest in reliability applications are designed to be repaired, rather than discarded, after they fail. This article begins with a very brief introduction to probabilistic modeling and statistical analysis of repairable systems, bolstered by many cross‐references and other references. There are several superficially striking similarities between reliability models for repairable systems and for nonrepairable items. These similarities should have led to great emphasis on the up‐to‐infinite differences in model interpretations. The article's stress, therefore, is on the consequences of the almost universal and chronic overemphasis on relatively unimportant similarities between concepts and models for nonrepairable items versus those for repairable systems. Unfortunately, most reliability books and papers continue to emphasize these avoidable ambiguities, which exacerbate the unavoidable confusion caused by inherent ambiguities. The ensuing vicious circle virtually ensures errors, especially when performing failure data analyses. Some of the mistakes in reliability books and papers are so blatant that they are obvious even from “eyeball analysis”!

show abstract

A set-of-numbers is NOT a data-set

Cited by 17 publications

References 12 publications

Applying Forgotten Lessons in Field Reliability Data Analysis to Performance-Based Support Contracts

Applying Forgotten Lessons in Field Reliability Data Analysis to Performance-Based Support Contracts

Generalized models of repairable systems: A survey via stochastic processes formalism

Repairable Systems Reliability

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