3D FMEA Monte-Carlo Reliability Simulator

Monte-Carlo Reliability Trials

Component failure rate 600/1000h

Trial length 2000 h

Time acceleration 6×

Trials run

Empirical success

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Analytic R(T)

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Current run

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How it works

Instead of just plotting the reliability formula, this scene runs the mission over and over. Each trial advances a shared mission clock; every component independently draws a fresh chance to fail in that instant, with a probability set by its failure rate λ (a Poisson process, so probability of failure in a small step dt is ≈ λ·dt). If a series component fails, or every unit in a redundant pair fails, the mission is lost and the run resets — otherwise it survives to trial length T and counts as a success. Run enough trials and the fraction of successes converges onto the exact analytic reliability from the series/parallel formulas.

P(fail in step dt) ≈ λ·dt   (Poisson process)
trial success  ⇔  every series stage survives to T
                   AND every redundant pair has ≥1 surviving unit
empirical R  →  R_series = ΠRᵢ   or   R_parallel = 1−Π(1−Rᵢ)  as trials → ∞

Failure rate — λ for the CO₂ scrubber node (per 1000 operating hours); the O₂ generator and power bus nodes run at a fixed, lower background rate for contrast.
Redundant toggle — duplicates the scrubber node into two parallel units; a trial only fails there if both fail before T.
Trial length — mission duration T each run must survive; longer trials give every component more chances to fail.
Time acceleration — how fast the simulated mission clock advances per real second, so you can watch trials accumulate quickly or slow it down to see one failure event clearly.

Real-world relevance: this repeated-trial approach is exactly how reliability engineers validate an analytic FMEA model — Monte-Carlo simulation of the same failure-rate assumptions should converge on the same number the series/parallel algebra predicts.

Operating Failed Mission clock

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