Deterministic versus Probabilistic

Abstract

Chris and Fred discuss what it means to be ‘deterministic’ versus ‘probabilistic’ … and what that means for reliability engineering. Know what these words mean and want to learn more? Don’t know what these words mean and want to understand how they could help reliability engineering? Listen to this podcast.

Key Points

Join Chris and Fred as they discuss what it means for us reliability engineers to think ‘deterministically’ versus ‘probabilistically.’

Topics include:

• Determinism means that everything has a cause and all outcomes can be completely defined (i.e. predicted) based on what has happened beforehand. There is no uncertainty, and hence no variation in what we observe.
• Probability describes the idea that for the same set of inputs or events, we can different outcomes … or variation in results.
• Two things can be true. Everything is deterministic. Everything happens for a reason. But we often have no hope of measuring all inputs. Think of fatigue cracking. We are pretty good at understanding how fatigue cracks propagate through steel. But to know exactly when a strut will fail due to fatigue, we need to know the precise location of every atom, every impurity, every surface crack, the mass of every car driving over that bridge and so on. We can’t ever do this. So we can combine deterministic models of fatigue cracking with probabilistic models to capture the uncertainty in the inputs to come up with something really useful.
• It all comes down to the decision you need to make. If you want to improve reliability, you need to know why your thing is going fail. Which means you need to have a deterministic model of why things fail. If you want to understand when your thing is likely to fail, you need a probabilistic model. Or a combination thereof. So what decision are you trying to make?

Enjoy an episode of Speaking of Reliability. Where you can join friends as they discuss reliability topics. Join us as we discuss topics ranging from design for reliability techniques to field data analysis approaches.

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Show Notes

 

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