Laxman Pangeni — New Contributor

Making the Right Choice in Reliability Engineering

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.

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Markov Chain Analysis and Eigenvector/Eigenvalue Problem: A Powerful Tool for Reliability Engineering

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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Unlocking Reliability: The Power of the Damage-Endurance Model in Product Development

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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Example from BDC Motor Reliability Assessment

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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