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title

FMEA

매 한 줄

"매 system 의 모든 failure mode 의 systematic enumeration + ranking". 1949 US Military (MIL-P-1629) → NASA Apollo → 자동차 (AIAG-VDA 2019, the modern standard) → 매 software / ML / SRE 의 risk-process 로 generalized. 매 "what can fail, how, what then, what to do" 의 매 4 column.

매 핵심

매 종류

DFMEA (Design): 매 product / component design 단계.
PFMEA (Process): 매 manufacturing / business process.
SFMEA (System): 매 system-of-systems 의 interaction.
FMECA: 매 + Criticality (quantitative).
MLFMEA / AI-FMEA (2024+): 매 ML model failure modes (data drift, prompt injection, hallucination).

매 AIAG-VDA 7-step (2019, current global standard)

Planning & Preparation (5T: InTent, Timing, Team, Tasks, Tools).
Structure Analysis (system → subsystem → component tree).
Function Analysis (each element 의 functions + interfaces).
Failure Analysis (Failure Effect FE / Failure Mode FM / Failure Cause FC chain).
Risk Analysis — replaces RPN with Action Priority (AP: H/M/L) based on (S, O, D).
Optimization (preventive + detection actions).
Results Documentation.

매 scoring

Severity (S) 1–10: 매 effect 의 customer / safety impact.
Occurrence (O) 1–10: 매 cause 의 likelihood.
Detection (D) 1–10: 매 control 의 detection ability (10 = 못 detect).
매 legacy RPN = S·O·D (deprecated by AIAG-VDA but still common).
매 modern Action Priority matrix: H / M / L.

💻 패턴

Minimal FMEA table (pandas)

import pandas as pd

rows = [
    {"item":"Brake pad","function":"friction","FM":"wear",
     "FE":"reduced braking","FC":"high mileage",
     "S":9,"O":4,"D":3},
    {"item":"Brake pad","function":"friction","FM":"contamination",
     "FE":"squeal","FC":"oil leak",
     "S":4,"O":3,"D":5},
]
df = pd.DataFrame(rows)
df["RPN"] = df.S * df.O * df.D
df = df.sort_values("RPN", ascending=False)

AIAG-VDA Action Priority

def action_priority(S, O, D):
    if S >= 9 and O >= 4: return "H"
    if S >= 9 and O >= 2: return "H"
    if S >= 7 and O >= 6 and D >= 6: return "H"
    if S >= 7 and O >= 4 and D >= 4: return "M"
    if S >= 4 and O >= 4: return "M"
    return "L"
df["AP"] = df.apply(lambda r: action_priority(r.S, r.O, r.D), axis=1)

Software-FMEA (microservice)

fmeas = [
    dict(component="auth-svc", FM="JWT signature mismatch",
         FE="login fails, downstream 401",
         FC="key rotation race",
         control="canary + jwks fallback",
         S=8, O=3, D=4),
    dict(component="auth-svc", FM="DB pool exhaustion",
         FE="latency spike, cascading 503",
         FC="connection leak in handler",
         control="bounded pool + timeouts + chaos test",
         S=7, O=5, D=6),
]

ML-FMEA (LLM application)

ml_fmeas = [
    dict(stage="prompt", FM="prompt injection",
         FE="data exfiltration via tool call",
         FC="user content concatenated unfiltered",
         control="structured prompt + injection classifier + tool allow-list",
         S=10, O=6, D=7),
    dict(stage="model",  FM="hallucinated citation",
         FE="false legal claim",
         FC="long-tail fact, no retrieval",
         control="RAG + post-hoc verifier",
         S=8,  O=7, D=5),
    dict(stage="data",   FM="distribution drift",
         FE="accuracy drop in prod",
         FC="seasonal user mix change",
         control="online metric monitor + canary",
         S=6,  O=6, D=4),
]

Criticality matrix plot

import matplotlib.pyplot as plt
plt.scatter(df.O, df.S, s=df.D*40, alpha=0.6)
for _, r in df.iterrows(): plt.annotate(r.FM, (r.O, r.S))
plt.xlabel("Occurrence"); plt.ylabel("Severity"); plt.grid()

매 결정 기준

상황	Approach
Hardware design (auto, aero)	DFMEA + AIAG-VDA
Manufacturing line	PFMEA
Safety-critical (DO-178C, ISO 26262)	FMEA + FTA + STPA
Software service	Software-FMEA + chaos engineering
LLM / ML system	ML-FMEA + red-team + evals
Quick triage	Risk matrix (S × O)

기본값: 매 AIAG-VDA 7-step + AP scoring (RPN deprecated).

🔗 Graph

부모: Risk_Management
변형: DFMEA · PFMEA · FMECA
응용: SRE
Adjacent: Chaos-Engineering

🤖 LLM 활용

언제: 매 new system 의 risk register 를 brainstorm; 매 architecture review 의 failure-chain 의 enumeration; 매 ML deployment 의 pre-mortem. 언제 X: 매 emergent / interactive failure (매 complex software) — 매 STPA 의 더 적합. 매 statistical reliability 는 FTA + Markov.

❌ 안티패턴

RPN multiplication only: 매 (10,1,1)=10 vs (2,5,1)=10 의 same — but severity 10 의 catastrophic. AP matrix 사용.
Sev/Occ/Det 의 inconsistent scale: 매 team-wide rubric 없으면 매 garbage.
One-shot document: 매 living document 가 아니면 매 outdated. 매 design change 의 trigger update.
Skipping detection actions: 매 only "add training" — 매 weak. 매 sensor / monitor / poka-yoke 의 추가.
Software FMEA 의 component-only: 매 interaction failures 의 missed — 매 STPA 의 complement.

🧪 검증 / 중복

Verified (MIL-P-1629; AIAG-VDA FMEA Handbook 2019; SAE J1739; ISO 26262-9).
신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1 placeholder
2026-05-10	Manual cleanup — 7-step AIAG-VDA + 5 patterns + ML-FMEA

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