Your Reliability Engineering Professional Development Site
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Mojan and Fred discuss the monumental reliability challenges of a mission to Mars. While the fundamental physics remains the same, they explore why success depends on a more rigorous application of existing methods, autonomous systems, and the shift toward heavy simulation and in-situ repairs.
Chris and Fred discuss which liner is best to use on a centrifugal pump, or make a general recommendation. How do you make a recommendation using reliability performance?
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Chris and Fred discuss how to analyze reliability data for repairable spares—and why the “as good as new” assumption can be dangerously misleading. They explore how maintenance itself can introduce new failures and distort your analysis.
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Mojan and Fred discuss how to integrate service plans into reliability target planning. They explore the shift from “worst-case” maintenance schedules to data-driven strategies, highlighting how predictable failure mechanisms and telemetry allow teams to take credit for high availability in their reliability models. ᐅ Play Episode
Mojan and Fred dive into the surprisingly complex world of the “paper jam,” a problem that remains a daily frustration despite decades of high-level scientific research. From Fred’s time at HP to the massive technical laboratories at Xerox, they explore why the interaction between rigid mechanisms and non-rigid, organic media remains one of the most difficult (and accepted) puzzles in reliability engineering.
Carl and Fred discuss a question from a reader about risk prioritization in FMEA procedure.
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Carl and Fred discuss the ongoing challenge between doing reliability work ourselves and enabling others to accomplish the work. And what is the future human role in the field of reliability, given the advancements in AI?
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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
Mojan and Fred tackle the common engineering dilemma of what to do when a test concludes with zero failures. They discuss how to “artificially” assume a failure to benchmark against existing models and how to determine if your product is actually better than expected or if your test was simply flawed.
Mojan and Fred discuss the strategic shift from modeling as a checkbox exercise to using RAM (Reliability, Availability, and Maintainability) models as a driver for proactive decision-making. They explore how modeling forces teams to confront “the weakest link” and align resources where they can have the most impact early in a program
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Fred shares insights from consulting with over 150 organizations on the perennial struggle between reactive “heroism” and proactive design. Mojan and Fred discuss the measurable benefits of investing in reliability early—such as reduced development time and lower warranty costs—and why 80% of companies still find themselves trapped in a “build-test-fix” cycle. ᐅ Play Episode
Mojan and Fred discuss the benefits of using failure models with testing. They explore the delicate balance between theoretical analysis and physical testing. While robust design starts with analysis, they discuss how testing serves as the ultimate “insurance policy” for decision-makers and why understanding the “Confidence Stack-up” is essential for a product’s success.
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Enrico and Fred discuss a listener’s question about using a single sample for testing.
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