Rebuttal To: A Critique Of The Reliability-analysis-center Failure-rate-model For Plastic Encapsulated Microcircuits

Denson, William

doi:10.1109/tr.1998.756084

Cited by 10 publications

(5 citation statements)

References 3 publications

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“…Reliability prediction approach was presented by Denson through analyzing the failure rate model of plastic encapsulated microcircuits. The approach depended upon the product development stages and its related reliability metric [11]. Various reliability prediction methods, their concepts of application, advantages, and disadvantages were investigated by Thakur & Sakravdia [12].…”

Section: Reliability Approaches and Indicesmentioning

confidence: 99%

Downtime Data Analysis Based on Maximum Likelihood (MLE) to Estimate Parameters of Reliability Distributions

Al‐Bossly¹

2020

Preprint

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Reliability analysis techniques are customary standard tools that are used for evaluating the performance of different equipment and devices in order to minimize their downtime. To predict the reliability, life data from a sample that is satisfactorily representative of the equipment should be fitted to the suitable statistical distribution. The parameterized distribution may be used to estimate essential characteristics such as failure rate; and probability at a precise time, as well as system reliability. In the current study, Weibull++/ALTA software package is used as a novel tool to fit the available data set to estimate the best fitted probability density function (PDF) using Maximum Likelihood (MLE) for parameter estimation. The determined distributions are then assessed using goodness-of-fit test to define how well it fits the available data set. There are multiple methods for determining goodness-of-fit. Weibull distributions and their special cases’ parameters have an effect on life times.

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Section: Reliability Approaches and Indicesmentioning

confidence: 99%

Downtime Data Analysis Based on Maximum Likelihood (MLE) to Estimate Parameters of Reliability Distributions

Al‐Bossly¹

2020

Preprint

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“…After that by using MM authors developed another model of the system in which system stops working and failed component immediately gets into the repair process. This new improved model works in, if repair time is less than the critical value, then system can return back in very short time period and so the repair time in this mode neglected as system seen as operational during this period and modelled by a Markov chain process (X(t), t ≥ 0), which is mathematically defined as X(t) = j ∀ j ∈ 1, 2, 3, …, 8 for the system is in state j (22) Then, reliability indexes of a system for both model are calculated and compared.…”

Section: Limitationsmentioning

confidence: 99%

“…MTTR is the usual time required to settle a fizzled item and return it to generation status [22]. The

MTTR

can be ascertained by separating the total time required for support by the aggregating number of repairs inside a particular time allotment.…”

Section: Reliability Prediction Metricsmentioning

confidence: 99%

Reliability analysis of safety‐critical and control systems: a state‐of‐the‐art review

2018

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In the past several decades, significant attention has been devoted to the quality assessment of safety-critical (SC) and control systems from many perspectives such as its reliability, safety, and performance. Researchers are continuing to put their efforts to ensure these dependability attributes. This study summarises the state of the art in the field of the reliability of such systems. A detailed literature survey is conducted to investigate the various techniques/models to ensure the reliability of the computer-based systems. The limitations of these models are also analysed with respect to their applicability in SC systems, for which a case study of nuclear power plant system has been taken. The direction for future research is suggested, based on the case study, to extend the further scope of research. 19 April 06 medium delay of 120 flights false alarm due to malfunction of software

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“…1 Many electronic gadgets and other industrial items have a short time when their failure rate is relatively high and then gradually decreases to a steady state, where it remains when aging triggers the rate to rise. 2 This is referred as U (bath-tub curve) shape failure rate. Workmanship flaws such as improper design, missing inspections, and mistreatment are reflected in failures on the left-hand edge of the bath-tub curve (check refs.…”

Section: Introductionmentioning

confidence: 99%

“…Several industrial devices have an increasing FR, although the rate of increase is seldom stable across the device's aging 1 . Many electronic gadgets and other industrial items have a short time when their failure rate is relatively high and then gradually decreases to a steady state, where it remains when aging triggers the rate to rise 2 . This is referred as U (bath‐tub curve) shape failure rate.…”

Section: Introductionmentioning

confidence: 99%

Reliability investigation of exponentiated Weibull distribution using IPL through numerical and artificial neural network modeling

Shafiq

Çolak

Sindhu

2022

Quality & Reliability Eng

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In current investigation, a novel implementation of intelligent numerical computing solver depending on artificial neural networks (ANN) has been provided to interpret failure function (FF), reliability function (RF), hazard rate function (HRF), Mils ratio (MR), and mean time to failure (MTTF). This study investigates a reliability model centered on the exponentiated Weibull distribution (EWD) and the inverse power law (IPL) model employing the ANN model. The nonmonotonic failure rate can be modeled via this distribution. A data set for the proposed ANN has been generated for various scenarios of (Exponentiated Weibull Inverse Power Law Distribution) EWIPLD model by variation of involved pertinent parameters via the Galerkin weighted residual method (GWRM). Levenberg–Marquard training algorithm has been used in the multi‐layer perceptron (MLP) network model developed with 10 nodes in the hidden layer. The Coefficient of Determination (R) value for the ANN model has been obtained as 0.9999. The findings obtained, revealed that ANNs are an excellent technique that can be applied to predict reliability measures in conjunction with the right statistical model.

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Rebuttal To: A Critique Of The Reliability-analysis-center Failure-rate-model For Plastic Encapsulated Microcircuits

Cited by 10 publications

References 3 publications

Downtime Data Analysis Based on Maximum Likelihood (MLE) to Estimate Parameters of Reliability Distributions

Downtime Data Analysis Based on Maximum Likelihood (MLE) to Estimate Parameters of Reliability Distributions

Reliability analysis of safety‐critical and control systems: a state‐of‐the‐art review

Reliability investigation of exponentiated Weibull distribution using IPL through numerical and artificial neural network modeling

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