AMSAA maturity projection model

Ellner, Paul M.; Wald, L.C.

doi:10.1109/rams.1995.513243

Cited by 21 publications

(33 citation statements)

References 6 publications

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“…Since then, a number of other methods have been developed. They include the delayed models [4-5, 11-12, 16-19], and the delayed / non-delayed models given in [8], [13], and [19].…”

Section: Introductionmentioning

confidence: 99%

Bayesian methods for evaluating discrete reliability growth

Hall

Mosleh

2010

2010 Proceedings - Annual Reliability and Maintainability Symposium (RAMS)

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“…Since then, a number of other methods have been developed. They include the delayed models [4-5, 11-12, 16-19], and the delayed / non-delayed models given in [8], [13], and [19].…”

Section: Introductionmentioning

confidence: 99%

Bayesian methods for evaluating discrete reliability growth

Hall

Mosleh

2010

2010 Proceedings - Annual Reliability and Maintainability Symposium (RAMS)

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“…Another projection model, termed the AMSAA Maturity Projection Model (AMPM), was introduced by Ellner, et al (1995). Like the AMSAA-Crow model, the AMPM applies to the case where the test duration is measured in a continuous fashion, such as in hours or miles.…”

Section: A Brief History Of Reliability Growth Projectionmentioning

confidence: 99%

“…Also, only FEFs associated with the surfaced modes need be referenced. In particular, unlike the methods in Crow (1982) and Ellner, et al (1995), no estimate of the arithmetic average of all the FEFs, that would be realized if all the B-modes were surfaced, is required. Another significant difference between the Stein approach and the other methods is that the Stein projection is a direct assessment of the realized system failure rate after failure mode mitigation.…”

Section: Differences In Technical Approachmentioning

confidence: 99%

“…Another significant difference between the Stein approach and the other methods is that the Stein projection is a direct assessment of the realized system failure rate after failure mode mitigation. The approaches (Crow, 1982), (Corcoran, et al, 1964), and (Ellner, et al, 1995) indirectly attempt to assess the realized system reliability by estimating the expected value of the mitigated system probability of failure or system failure rate, r(T), where r(T) is viewed as a random variable. For example, in Crow (1982) and Ellner, et al (1995) , the realized value of r(T) is assessed as the estimate of a conditional (given Ai), or unconditional expected value of r(T), respectively, where,…”

Section: Differences In Technical Approachmentioning

confidence: 99%

“…for large kB ). The use of B-mode first occurrence times to estimate I ,/3 and a allows the AMPM approach (Ellner, et al, 1995) to be used to assess the realized value of r(t) for t > T. One need only assume that all fixes are incorporated by t. In particular, it is not necessary to assume that all fixes are delayed until t. Note, however, the AMPM and AMSAA-Crow methods both require an assessment of Pud, the arithmetic average of the B-mode FEFs that would be realized if all B-modes were surfaced. Also, both these methods utilize the number of observed B-modes and the B-mode first occurrence times for parameter estimation to obtain an assessment of the realized mitigated system failure rate.…”

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confidence: 99%

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AMSAA maturity projection model based on stein estimation

Ellner¹,

Hall²

Annual Reliability and Maintainability Symposium, 2005. Proceedings.

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Reliability‐Growth Concepts and Testing

Cushing

2018

Wiley Encyclopedia of Electrical and Electronics Engineering

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An engineered product is said to experience reliability growth when (1) a failure mode is observed and investigated until the root cause is identified and (2) an understanding of the root cause failure mechanism is then developed and used to formulate and implement a corrective action that reduces or eliminates the probability or frequency of the failure mode's recurrence within the product population. This article addresses how one identifies failure modes during developmental product testing, develops an understanding of them, and formulates corrective actions to mitigate them. A brief introduction to statistical reliability‐growth modeling is provided, along with lessons learned from its application to complex repairable assemblies and products. Statistical reliability‐growth modeling was developed to pool data across evolving product configurations as failure modes are observed and mitigated during a sequence of developmental tests. In most applications, one does not have sufficient data to generate final configuration estimates of satisfactory uncertainty; the pooling of data from current and prior configurations, using a method that accounts for the impact of corrective actions, can provide a clearer understanding of reliability growth and support improved product development decision‐making (e.g., when to grant production approval). The most crucial lesson learned from several decades of applications is that successful reliability‐growth programs require that all but the most elusive failure modes be identified and mitigated prior to product‐level reliability‐growth testing; this key engineering activity enables the product to meet the high level of initial reliability required for success.

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AMSAA maturity projection model

Cited by 21 publications

References 6 publications

Bayesian methods for evaluating discrete reliability growth

Bayesian methods for evaluating discrete reliability growth

AMSAA maturity projection model based on stein estimation

Reliability‐Growth Concepts and Testing

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