Bathtub Curve and MTBF
A site dedicated to data-driven reliability decisions
Topics:
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1.Reliability
2.Reliability Metrics
3.Decisions
4.Validation
Decision
Mission Reliability
MTBF
(3)(1)(2)
Topics:​
Terms/Concepts
For the disambiguation and clarification purpose, in addition to the standard or the affirmative way of definitions, NEGATIONS (apophasis) as well as CONTRASTS and DISTINCTIONS are also introduced.
1.Reliability
Definition
Reliability is the likelihood of an objective to be achieved for a product to function satisfactorily in field.
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Contrasts and Distinctions
-as LIKELIHOOD, not probability
-per SUCCESSES, where a success is an OUTCOME, not an event
-the secondary and eh top level issue​
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Mission Reliability
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​-The RIGHT Decision: The right decision means it is a valid one with possible human errors removed. In addition, all decisions are purpose driven. Therefore, the right decision also means that it serves the right purpose as well as interests of the right party or parties.
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Likelihood vs probability
-Likelihood: statistics, estimates of real values, ONLY about population
-probability: based on the assumption of known real values for populations, ONLY about individuals or a group of individuals
2.Reliability Metrics
MTBF
a reliability metric, especially for products or systems exhibiting failures in a shape of a bathtub curve. Information on MTBFs supports decision making for engineering development and service operations in management and planning.
Contrasts and Distinctions
​-characteristic
-MTTF vs MTBF
3.Decisions
Definition
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Contrasts and Distinctions
-Verification vs validation
-Reliability metrics
​Data Driven Decisions​​
Evidence Based Decisions
​Decision-making Questions
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The RIGHT Decision
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The right decision means it is a valid one with possible human errors removed. In addition, all decisions are purpose driven. Therefore, the right decision also means that it serves the right purpose as well as interests of the right party or parties.
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Uncertainty (or Unknow & Uncertainty)
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A. Aleatory Uncertainty (Variability)
This is the "property variation" you mentioned. If you pick one apple from a tree, its weight is uncertain before you pick it because every apple on the tree is different. This is inherent randomness in the system.
B. Epistemic Uncertainty (Lack of Knowledge)
This is uncertainty due to things we could know but don't. Even if you pick the exact same apple (no more variation), your measurement of it will still be uncertain because your scale isn't perfect.
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4.Validation
Definition
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​2.Contrasts and Distinctions
-Verification vs validation
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