Probability of infancy problems for space launch vehicles

Guikema, Seth D.; Patè‐Cornell, M. Elisabeth

doi:10.1016/j.ress.2004.06.001

Cited by 25 publications

(8 citation statements)

References 5 publications

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“…These events are often discrete in nature (e.g., a system fails or works, a person contracts a disease or does not) and, in many cases, past data about the occurrences of these events are available. For example, within engineering risk analysis, discrete events and count data are common problems in areas such as modeling pipe breaks in water distribution systems (Andreou et al , 1987a, 1987b), modeling power outages during hurricanes (Liu et al , 2005; Han et al , submitted), modeling traffic accidents (e.g., Lord et al , 2005), and modeling space system reliability (Guikema & Paté‐Cornell, 2004, 2005). In situations such as these, an analyst wishes to draw inferences about the likelihood of the occurrence of a discrete event or the reliability of a system on the basis of observations of counts of events in the past.…”

Section: Introductionmentioning

confidence: 99%

A Flexible Count Data Regression Model for Risk Analysis

Guikema

Goffelt²

2008

Risk Analysis

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148

107

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In many cases, risk and reliability analyses involve estimating the probabilities of discrete events such as hardware failures and occurrences of disease or death. There is often additional information in the form of explanatory variables that can be used to help estimate the likelihood of different numbers of events in the future through the use of an appropriate regression model, such as a generalized linear model. However, existing generalized linear models (GLM) are limited in their ability to handle the types of variance structures often encountered in using count data in risk and reliability analysis. In particular, standard models cannot handle both underdispersed data (variance less than the mean) and overdispersed data (variance greater than the mean) in a single coherent modeling framework. This article presents a new GLM based on a reformulation of the Conway-Maxwell Poisson (COM) distribution that is useful for both underdispersed and overdispersed count data and demonstrates this model by applying it to the assessment of electric power system reliability. The results show that the proposed COM GLM can provide as good of fits to data as the commonly used existing models for overdispered data sets while outperforming these commonly used models for underdispersed data sets.

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Section: Introductionmentioning

confidence: 99%

A Flexible Count Data Regression Model for Risk Analysis

Guikema

Goffelt²

2008

Risk Analysis

Self Cite

148

107

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“…For example, the Atlas launch vehicle had the first ten failures occurring within the first 19 flights, so we took a failure rate of 10/19 = 0.53 as being representative of the initial failure rate. Others have analyzed the same data using other approaches, including Bayesian [19,20] and frequentist [21,22]. Not surprisingly, the different methods have resulted in significantly different estimates of the initial failure rate, both higher and lower than the results we obtained using a running snapshot.…”

Section: Historical Failure Data For Launch Vehiclesmentioning

confidence: 68%

Developing probabilistic safety performance margins for unknown and underappreciated risks

Benjamin

Dezfuli²,

Everett

2016

Reliability Engineering & System Safety

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Probabilistic safety requirements currently formulated or proposed for space systems, nuclear reactor systems, nuclear weapon systems, and other types of systems that have a low-probability potential for high-consequence accidents depend on showing that the probability of such accidents is below a specified safety threshold or goal. Verification of compliance depends heavily upon synthetic modeling techniques such as PRA. To determine whether or not a system meets its probabilistic requirements, it is necessary to consider whether there are significant risks that are not fully considered in the PRA either because they are not known at the time or because their importance is not fully understood. The ultimate objective is to establish a reasonable margin to account for the difference between known risks and actual risks in attempting to validate compliance with a probabilistic safety threshold or goal. In this paper, we examine data accumulated over the past 60 years from the space program, from nuclear reactor experience, from aircraft systems, and from human reliability experience to formulate guidelines for estimating probabilistic margins to account for risks that are initially unknown or underappreciated. The formulation includes a review of the safety literature to identify the principal causes of such risks.

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“…The significant variation in probability of failure for the early portion of a new launch vehicle's flight history both proves through past examinations of observed launch vehicle success rates 3,4 and demonstrates that a single vehicle throughout its flight history cannot be modeled as a Bernoulli trial. The Bivariate Approach removes this assumption of Bernoulli trials, allowing for greater insight by examining the flight history as the specific realization of independent flights of intrinsically dependent trials.…”

Section: The Bivariate Approachmentioning

confidence: 89%

Probability of Failure Estimation for New Vehicles Using the Bivariate Approach to Learning

Draper¹

2010

48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

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New launch vehicles are inherently ultra-hazardous yet often demonstrate rapid and significant gains in the probability that they will successfully complete later missions. Such gains are alluded to by the probability of failure approach recommended by the Common Standards Working Group and codified within Part 417 of the Federal Aviation Administration regulations. While such improvements in the probability of success for a new launch vehicle are well documented, very few analytical techniques exist to directly model this phenomenon. The Bivariate Approach is one such model that has been applied to the Ecosse 6, a generic new vehicle developed by a new operator. This paper examines probability of failure estimates for the Ecosse 6 vehicle developed using the Bivariate Approach given a range of similar vehicles and a hypothetical flight history that sees the vehicle failing on the first of five flights. For this hypothetical flight history, probability of failure estimates using techniques accepted by the CSWG may be up to 400% more conservative than those developed with the Bivariate Approach. This conservatism is identified herein as being sufficient to deny a vehicle like the Ecosse 6 from completing a mission to the International Space Station when the Bivariate Approach indicates that the risks would be well below accepted levels. NomenclatureVehicle  = vehicle weighting B = Bivariate Distribution B S = Similarity Bivariate B 0 = Preflight Bivariate E c = Expected Casualties i I k , = innovation parameter i R k , = relevance parameter L  = learning rate L = Learning p = probability of success p 0 = initial probability of success  p = maximum probability of success  = Preflight Bivariate weighting

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Probability of infancy problems for space launch vehicles

Cited by 25 publications

References 5 publications

A Flexible Count Data Regression Model for Risk Analysis

A Flexible Count Data Regression Model for Risk Analysis

Developing probabilistic safety performance margins for unknown and underappreciated risks

Probability of Failure Estimation for New Vehicles Using the Bivariate Approach to Learning

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