Warranty calculations for missiles with only current-status data, using Bayesian methods

Olwell, David H.; Sorell, A.A.

doi:10.1109/rams.2001.902455

Cited by 18 publications

(22 citation statements)

References 5 publications

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“…Rate of outcome = Outcome × 100 (10) The failure data for each subsystem has been weighed (w) against its variance, and it is calculated using…”

Section: Meta-analysis Test Stepsmentioning

confidence: 99%

“…To this day, research articles highlighting analytical methods on small data sets are practically limited. D.H. Olwell et.al [10] completed limited data with advanced information using the Weibull probability distribution. The paper conducted a firing analysis of 2000 motors used in missiles in field conditions using the Maximum Likelihood Estimation (MLE) method and the Bayesian method [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…D.H. Olwell et.al [10] completed limited data with advanced information using the Weibull probability distribution. The paper conducted a firing analysis of 2000 motors used in missiles in field conditions using the Maximum Likelihood Estimation (MLE) method and the Bayesian method [10][11][12]. R M Mayer et.al [13] pooled the data from multiple data sets to get a large amount of failure data for statistical interpretation.…”

Section: Introductionmentioning

confidence: 99%

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Statistical Reliability Assessment for Small Sample of Failure Data of Dumper Diesel Engines Based on Power Law Process and Maximum Likelihood Estimation

et al. 2021

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Dumpers or dump trucks are used all over the world to move overburden from many opencast mines. Diesel engines are the main driving force behind the trucks. The frequency of damage due to the failure of diesel engines is enormous. Therefore, efforts are necessary to analyze failure to reduce the downtime periods. A detailed analysis of engine failure at the subsystem level needs to be done. Reliability analysis and maintenance planning remain the norm in this regard. The obstacle faced while analysing the reliability of dumpers was the availability of a large number of data failures. In this paper, this issue is addressed by using Common Beta Hypothesis test and Meta-analysis test. The engine is divided into five subsystems. The result shows that all five subsystems pass the CBH test and Meta-analysis test. Accordingly, the failure data is grouped. The trend test of grouped failure data shows that the Failure data of two subsystems follows the independent and identically distributed characteristics while the remaining three do not follow it. The reliability is estimated for all five subsystems. Finally, fuel supply subsystems show the highest reliability while the lowest value is seen for self-starting subsystems.

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“…Rate of outcome = Outcome × 100 (10) The failure data for each subsystem has been weighed (w) against its variance, and it is calculated using…”

Section: Meta-analysis Test Stepsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Statistical Reliability Assessment for Small Sample of Failure Data of Dumper Diesel Engines Based on Power Law Process and Maximum Likelihood Estimation

et al. 2021

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“…Several exceptions are worth mentioning. Olwell and Sorell (2001) show how to supplement limited field failure data with prior information to estimate a Weibull lifetime distribution. Hamada (2005) describes a Bayesian analysis of repeated-measures degradation data from a laser life test in which the slopes of the degradation paths vary from laser to laser.…”

Section: Related Workmentioning

confidence: 99%

Application of Bayesian Methods in Reliability Data Analyses

Meeker

2014

Journal of Quality Technology

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The development of the theory and application of Monte Carlo Markov Chain methods, vast improvements in computational capabilities and emerging software alternatives have made it possible for more frequent use of Bayesian methods in reliability applications. Bayesian methods, however, remain controversial in Reliability (and some other applications) because of the concern about where the needed prior distributions should come from. On the other hand, there are many applications where engineers have solid prior information on certain aspects of their reliability problems based on physics of failure or previous experience with the same failure mechanism. For example, engineers often have useful but imprecise knowledge about the effective activation energy in a temperature-accelerated life test or about the Weibull shape parameter in the analysis of fatigue failure data. In such applications, the use of Bayesian methods is compelling as it offers an appropriate compromise between assuming that such quantities are known and assuming that nothing is known. In this paper we compare the use of Bayesian methods with the traditional maximum likelihood methods for a group of examples including the analysis of field data with multiple censoring, accelerated life test data, and accelerated degradation test data. The development of the theory and application of Monte Carlo Markov Chain methods, vast improvements in computational capabilities and emerging software alternatives have made it possible for more frequent use of Bayesian methods in reliability applications. Bayesian methods, however, remain controversial in Reliability (and some other applications) because of the concern about where the needed prior distributions should come from. On the other hand, there are many applications where engineers have solid prior information on certain aspects of their reliability problems based on physics of failure or previous experience with the same failure mechanism. For example, engineers often have useful but imprecise knowledge about the effective activation energy in a temperature-accelerated life test or about the Weibull shape parameter in the analysis of fatigue failure data. In such applications, the use of Bayesian methods is compelling as it offers an appropriate compromise between assuming that such quantities are known and assuming that nothing is known. In this paper we compare the use of Bayesian methods with the traditional maximum likelihood methods for a group of examples including the analysis of field data with multiple censoring, accelerated life test data, and accelerated degradation test data.

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“…Therefore, the data from such devices consist of both left‐censored (failure) and right‐censored (success) observations at prefixed inspection time points, and the observations are binary data (failure or not) instead of real failure time data. Other examples of one‐shot devices are automobile airbags, missiles (Olwell and Sorell), and fire extinguishers (Newby). This kind of data is also known as current status data in the survival analysis literature.…”

Section: Introductionmentioning

confidence: 99%

Robust inference for one‐shot device testing data under exponential lifetime model with multiple stresses

Castilla¹,

Martín²,

Pardo³

2020

Quality & Reliability Eng

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Introduced robust density-based estimators in the context of one-shot devices with exponential lifetimes under a single stress factor. However, it is usual to have several stress factors in industrial experiments involving one-shot devices. In this paper, the weighted minimum density power divergence estimators (WMDPDEs) are developed as a natural extension of the classical maximum likelihood estimators (MLEs) for one-shot device testing data under exponential lifetime model with multiple stresses. Based on these estimators, Wald-type test statistics are also developed. Through a simulation study, it is shown that some WMDPDEs have a better performance than the MLE in relation to robustness. Two examples with multiple stresses show the usefulness of the model and, in particular, of the proposed estimators, both in engineering and medicine.

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Warranty calculations for missiles with only current-status data, using Bayesian methods

Cited by 18 publications

References 5 publications

Statistical Reliability Assessment for Small Sample of Failure Data of Dumper Diesel Engines Based on Power Law Process and Maximum Likelihood Estimation

Statistical Reliability Assessment for Small Sample of Failure Data of Dumper Diesel Engines Based on Power Law Process and Maximum Likelihood Estimation

Application of Bayesian Methods in Reliability Data Analyses

Robust inference for one‐shot device testing data under exponential lifetime model with multiple stresses

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