Reliability Engineering Methods
Reliability Engineering Methods - Related Videos
09:38
Scaling of Engineered Vascular Grafts Using 3D Printed Guides and the Ring Stacking Method
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Cited by 6 •
2017
Scalable engineered blood vessels would improve clinical applicability. Using easily sizable 3D-printed guides, rings of vascular smooth muscle were created and stacked into a tubular form, forming a vascular graft. Grafts can be sized to meet the range of human coronary artery dimensions by simply changing the 3D-printed guide size.
09:20
Reliably Engineering and Controlling Stable Optogenetic Gene Circuits in Mammalian Cells
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Cited by 4 •
2021
Reliably controlling light-responsive mammalian cells requires the standardization of optogenetic methods. Toward this goal, this study outlines a pipeline of gene circuit construction, cell engineering, optogenetic equipment operation, and verification assays to standardize the study of light-induced gene expression using a negative-feedback optogenetic gene circuit as a case study.
01:29
Reliability and Validity
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2020
Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways. Unfortunately, being consistent in measurement does not necessarily mean that you have measured something correctly. To illustrate this concept, consider a kitchen...
05:13
Reliability in Psychology Experiments
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2023
Source: Laboratories of Gary Lewandowski, Dave Strohmetz, and Natalie Ciarocco—Monmouth University In order to study something scientifically, a researcher needs to determine a way to quantify it. However, psychological constructs can be challenging to measure and quantify. This video examines reliability in the context of content analysis. A recent study in the journal Pediatrics reported that 4-year-olds who watched a fast-paced cartoon had worse performance on cognitive tasks, such as...
07:12
A Standard and Reliable Method to Fabricate Two-Dimensional Nanoelectronics
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Cited by 6 •
2018
The article aims to introduce a standard and reliable fabrication procedure for the development of future low dimensional nanoelectronics.
FAQs
Censored observations indicate that a component’s exact failure time is not available, even though the observation still contributes information. Reliability analysis incorporates these records alongside observed failure times rather than treating them as failures at the test endpoint. This distinction helps preserve statistical evidence when testing ends before every product fails and supports more defensible performance estimates.
The hazard rate describes the likelihood of failure in relation to time and provides a time-dependent view that complements overall reliability. Examining changes in hazard can help characterize when failures become more or less likely during a product’s life. This information supports comparisons among designs and helps connect statistical failure behavior with maintenance or improvement decisions.
Weibull analysis applies a probability distribution to failure-time data so engineers can examine patterns in product life and estimate reliability-related behavior. Its value comes from translating observed failures and, where available, censored records into a structured statistical model. The resulting analysis can inform durability assessment, design improvements, and comparisons between product or component populations.
Fault tree analysis and failure mode and effects analysis address risk from complementary directions. Fault tree analysis traces how combinations of faults can lead to an undesirable system outcome, whereas failure mode and effects analysis examines potential failure modes and their effects. Using either method, or both, helps identify risks that may warrant design changes or quality-control attention.
A practical workflow begins by defining the required function, time period, and operating conditions, then collecting reliability or failure-time information from testing or field observations. Analysts evaluate failure times, censored observations, probability distributions, hazard rates, or related measures such as mean time to failure. The findings are then used to guide maintenance, design, quality, or lifecycle decisions.
Reliability testing and life data analysis are useful when teams need evidence about how long products, systems, or components perform before failure under defined conditions. Testing supplies observations, while statistical analysis organizes those observations to estimate performance and reveal failure patterns. These methods support decisions about durability, safety, design refinement, and expected lifecycle costs.
Failure-time results and hazard-rate information can help organizations decide when maintenance attention may be appropriate rather than relying only on arbitrary intervals. Mean time to failure or mean time between failures provides additional summaries for planning, although the relevant measure depends on the system and observation context. The analysis connects statistical evidence with operational and lifecycle decisions.
This topic applies statistical reasoning to incomplete and time-dependent performance data, including failure times and censored observations. Probability distributions, hazard rates, Weibull analysis, and life data analysis turn those observations into interpretable evidence about product or system behavior. In turn, the evidence supports risk assessment, quality control, safety decisions, and improvements across a product’s lifecycle.