Improvements in reliability assessment and prediction methodology

Johnson, B.G.; Gullo, Louis J.

doi:10.1109/rams.2000.816304

Cited by 10 publications

(2 citation statements)

References 3 publications

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“…However, the development of such methods requires more effort [15]. Instead of assessing the system on the component level, some approaches use a top-down approach in which the field-reliability of new systems is estimated from field-data of similar systems in operation [8,9].…”

Section: Literature Reviewmentioning

confidence: 99%

Field-Reliability Predictions Based on Statistical System Lifecycle Models

Felsberger

Kranzlmüller

Todd

2018

Lecture Notes in Computer Science

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Reliability measures the ability of a system to provide its intended level of service. It is influenced by many factors throughout a system life-cycle. A detailed understanding of their impact often remains elusive since these factors cannot be studied independently. Formulating reliability studies as a Bayesian regression problem allows assessment of their impact simultaneously and to identify a predictive model of reliability metrics. The proposed method is applied to currently operational particle accelerator equipment at CERN. Relevant metrics were gathered by combining data from various organizational databases. To obtain predictive models, different supervised machine learning algorithms are applied and compared in terms of their prediction error and reliability. Results show that the identified models accurately predict the meantime between failure of devices-an important reliability metric for repairable systems-and reveal factors which lead to an increased dependability. These results provide valuable inputs for early development stages of highly dependable equipment for future particle accelerators.

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Section: Literature Reviewmentioning

confidence: 99%

Field-Reliability Predictions Based on Statistical System Lifecycle Models

Felsberger

Kranzlmüller

Todd

2018

Lecture Notes in Computer Science

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“…Compared with RGT, few papers have been published to address RGP related to design, manufacturing, software, and process issues for electronic equipment. Johnson & Gullo [19] and Gullo [20] reported an in-service reliability prediction tool HIRAP developed by Honeywell engineers. HIRAP breaks failures into seven categories among which category 1-5 consist of hardware failures and categories 6 and 7 are used to address process, manufacturing and design errors.…”

Section: Challenges In Reliability Managementmentioning

confidence: 99%

A multi-objective decision making on reliability growth planning for in-service systems

Jin

Wang

2009

2009 IEEE International Conference on Systems, Man and Cybernetics

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New designs prioritized for the fast time-to-market usually can not carry out sufficient in-house reliability growth testing due to the stringent delivery deadline. Reliability improvement for those systems can be achieved by implementing corrective actions (CAs) on in-service systems. In this paper, three types of effectiveness functions are proposed to measure the reduction rate of failure modes given different CA resources. Integrated with the effectiveness function, a new failure intensity model is proposed for predicting the mean-time-between-failures (MTBF) of field systems. Finally, a multi-objective optimization model is formulated to maximize the system reliability and to minimize the reliability uncertainty with the constraint of the CA resources. Genetic Algorithms combined with greedy heuristic are applied to search the optimal CA decisions that lead to the maximum reliability growth while minimizing the reliability uncertainty. Results show that the proposed reliability growth program can effectively guide decision-makers to find the most effective corrective actions for achieving the reliability goal for a large fleet of in-service systems. Throughout paper, systems and products will be used interchangeably.

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Novel concepts for reliability technology

Ryu

Chang

2005

Microelectronics Reliability

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Improvements in reliability assessment and prediction methodology

Cited by 10 publications

References 3 publications

Field-Reliability Predictions Based on Statistical System Lifecycle Models

Field-Reliability Predictions Based on Statistical System Lifecycle Models

A multi-objective decision making on reliability growth planning for in-service systems

Novel concepts for reliability technology

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