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Enrico Belmonte — New Contributor

Author of the Reliability Competence article series. This author's archive lists contributions to articles and episodes.

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SOR 1160 Data Gathering Challenges

SOR 1160 Data Gathering Challenges

Data Gathering Challenges

Abstract

Enrico and Fred discuss discuss the challenges of data collection in reliability engineering, prompted by a listener working in asset reliability. They explore why gathering useful data is often the most difficult step, how modern connected systems are changing data availability, and why defining the purpose of analysis is essential before collecting data. The discussion also highlights organizational barriers, data quality issues, and the growing role of AI in supporting data analysis. ᐅ Play Episode

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SOR 1058 Use and Environmental Data

SOR 1058 Use and Environmental Data

How to use data from connected devices for derivation of mission profiles

Abstract

Enrico and Fred discuss the value of using real-world usage data to define mission profiles, advocating for a shift from deterministic to stochastic approaches. They touch on the impact of environmental conditions, the risk of selection bias in data analysis, and weigh the cost-benefit of using connected devices to improve design and reliability decisions.
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Why Various Distributions Are Appropriate

Why Various Distributions Are Appropriate

Why are Weibull, lognormal, and exponential distributions more appropriate than others for modeling failure distributions?

In this article, I want to show you why Weibull, lognormal, and exponential distributions are more appropriate than others for modeling failure distributions. Instead of starting from the explanation of the most used fitting distributions in reliability engineering, I will begin with the analysis of artificial datasets created assuming specific failure behaviors. In other words, I will start from the data and not from the statistical distributions. I think that this approach can help those who deal with this topic for the first time.

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Stochastic Nature of Stress and Strength

Stochastic Nature of Stress and Strength

Since I published my first contribution about reliability, I’ve received a number of emails asking where the stress and strength distributions come from (Figure 1).

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