Your Reliability Engineering Professional Development Site
In today’s fast-paced engineering landscape, building products that are not only innovative but also resilient is critical. "Reliability by Design" is a comprehensive article series that explores the principles, methodologies, and best practices of Design for Reliability (DfR). This series delves into how reliability can be systematically integrated into the design process from concept to deployment, covering topics such as Failure Modes and Effects Analysis (FMEA), Physics of Failure (PoF), robust design techniques, and predictive reliability modeling. Whether you're a reliability engineer, product designer, or quality professional, this series will provide actionable insights to help you develop products that stand the test of time.
When we think of reliability tools, the FMEA often takes the spotlight. But if you’re skipping the P-Diagram, you’re likely missing the very foundation of failure prevention.
In my 15+ years working on field reliability and failure analysis, I’ve come to view the Parameter Diagram (P-Diagram) as the unsung hero of robust design.
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When we think of reliability tools, the FMEA often takes the spotlight. But if you’re skipping the P-Diagram, you’re likely missing the very foundation of failure prevention.
In my 15+ years working on field reliability and failure analysis, I’ve come to view the Parameter Diagram (P-Diagram) as the unsung hero of robust design.
[Read more…]
In reliability engineering, we often need to extrapolate test data collected under accelerated stress conditions to predict performance under normal operating conditions. Two mathematical models commonly used for this purpose are the power-law model and the exponential model. But which one should you choose when your data fits both models equally well? This article explores the differences between these models and provides practical guidance on making this critical decision.
In reliability engineering, predicting system behavior over time is crucial for maintenance planning and risk assessment. One powerful mathematical tool for this analysis is Markov Chain modelling. In this article, I’ll demonstrate how Markov Chains can predict device reliability using a real-world example: battery reliability in reliability testing facilities.
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Reliability is at the heart of robust product design. Engineers and reliability professionals continuously seek ways to predict, assess, and improve product longevity. One fundamental approach to achieving this is the Damage-Endurance Model, a powerful tool in reliability engineering that helps quantify failure risks and optimize designs.
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Reliability analysis is essential for ensuring long-term performance of hardware components. However, predicting failures with small sample sizes is a challenge. Traditional statistical methods often require large datasets, whereas Bayesian statistics can incorporate prior knowledge to improve predictions by updating beliefs as new data becomes available.
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In reliability engineering and data-driven maintenance strategies, understanding different failure modes is crucial for designing robust systems. Survival analysis is a powerful statistical tool that allows us to analyze time-to-event data and assess the reliability of components over time. In this article, we’ll explore how survival analysis can be applied to multi-modal failure scenarios using R.
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