Add common-cause failures: beta-factor model (#44) - #65
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derrynknife merged 2 commits intoJul 21, 2026
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Redundant components often fail from a shared root cause, so their failures are correlated; the exact engine assumes independence and over-estimates redundant systems. Add the beta-factor CCF model: a fraction beta of a symmetric group's failures come from a cause shared across the group (failing every member at once), the rest are independent. NonRepairableRBD gains a `ccf_groups` argument taking CCFGroup(members, BetaFactor(beta)). System reliability is computed *exactly* by conditioning on each group's shared-cause event (fires -> whole group down; else each member fails only independently) and summing the 2**groups branches, each an ordinary independent system-probability evaluation. So beta=0 reproduces the independent result exactly and beta=1 collapses a group to a single component. - New repyability/rbd/ccf.py: BetaFactor and CCFGroup, with validation. - NonRepairableRBD: ccf_groups validated (members exist, non-repeated, not in/out, disjoint, symmetric); sf/ff route through the conditioning engine; RBD serialisation captures the groups. - The probability-dependent importance/sensitivity and condition-based methods raise a clear NotImplementedError on a CCF RBD (CCF is honoured by sf/ff); structural_importance is probability-free and stays available. - Tests anchor beta=0 == independent and the exact 2-parallel conditioning against the textbook beta*Q + ((1-beta)Q)^2, plus multiple groups, time-varying, serialisation and validation. Docs (guide + api) and CHANGELOG updated. Alpha-factor / MGL (partial common-cause) are a planned extension. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01NUGaeQXKxjjiQ1ULT1ApXS
Extend the CCF support with the Multiple Greek Letter model alongside beta-factor. MGL captures partial common causes (a cause failing some but not all of a group) via a cascade of conditional probabilities beta, gamma, delta, ...; the number of letters fixes the group size (m-1 letters for m members). MGL(beta) on two members is exactly BetaFactor(beta). - New MGL model (repyability/rbd/ccf.py) with the standard MGL basic-event probabilities Q_k for a specific k-of-m subset. Both CCF models now share a decompose(members, Q) -> (independent failure, mutually-exclusive shocks) interface, and NonRepairableRBD's conditioning engine sums over each group's shock-outcome partition (generalising the beta-factor fires/not branches), reusing the ordinary independent engine per branch. - Serialisation and package export extended; group-size validation for MGL. - Tests: MGL(beta) == BetaFactor(beta) on two members; a 1-of-3 triple matches the textbook leading-order MGL result; gamma=1 collapses to all-or-nothing; serialisation round-trip; validation. Docs and CHANGELOG updated. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01NUGaeQXKxjjiQ1ULT1ApXS
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Summary
Adds common-cause failures (CCF) to
NonRepairableRBDvia the beta-factor model (#44). Redundant components often fail from a shared root cause, so their failures are correlated; the exact engine assumes independence and over-estimates redundant systems. Beta-factor splits a symmetric group's failures: a fractionbetacome from a cause shared across the group (failing every member at once), the rest are independent.It reuses the exact engine — no rewrite. System reliability is computed by conditioning on each group's shared-cause event (fires → the whole group is down; doesn't → each member fails only independently) and summing the
2**groupsbranches, each an ordinary independentsystem_probabilityevaluation. So:beta = 0reproduces the independent result exactly,beta = 1collapses a redundant group to a single component,beta*Q + ((1-beta)Q)^2(to higher order — the conditioning is exact).Type of change
Checklist
black,isort,flake8, andmypypasscoverage run -m pytestpasses and stays above the coverage gateCHANGELOG.mdupdated under[Unreleased]Notes for reviewers
sf()/ff()(and quantities derived from them). The probability-dependent importance/sensitivity measures and the condition-based (age) methods raise a clearNotImplementedErroron a CCF RBD rather than silently returning independent results;structural_importance, being probability-free, is unaffected and still works. This keeps the first pass honest.rbd.pyis unchanged; all wiring is inNonRepairableRBD+ a smallccf.py.🤖 Generated with Claude Code
https://claude.ai/code/session_01NUGaeQXKxjjiQ1ULT1ApXS
Generated by Claude Code