Flight Test Data Analysis and Anomaly Detection
Analyze flight test telemetry, detect anomalies, and generate root cause assessments
What You Can Do
You can rapidly examine thousands of instrumentation parameters across multiple test points to isolate unexpected behaviors and their origins. Claude structures the detective work: asking conditional questions about parameter relationships, flagging impossible states, isolating anomaly emergence moments, and building logical chains connecting symptoms to root causes. The output produces defensible technical justifications suitable for FAA or military certification packages and flight test reports.
Features
identifies deviations from baseline normal operation using quantitative methods
cross-references secondary systems (hydraulics, electrics, thermal) with primary flight control responses to isolate interdependencies
distinguishes sensor failures from actual aircraft anomalies through logical consistency checks
develops evidence-based cause chains with supporting data points and conditional logic
produces technical assessments formatted for FAA memoranda and military qualification packages
compares nominal vs. off-nominal test runs to isolate what changed between flights
identifies the precise moment anomalies emerge and tracks parameter sequences leading to the event
generates preliminary assessments to inform design changes, waivers, and test modifications
Example Output
Example 1: Hydraulic System Anomaly
Input: Flight test point data showing unexpected pressure spike in System B at 45 seconds into climb.
Output:
- Anomaly Confirmed: System B pressure 2.3σ above baseline (3,450 psi vs. nominal 3,100–3,200 psi)
- Symptom Timeline: Pressure spike preceded pitch control lag by 0.8 seconds
- Correlated Parameters: Pump displacement at nominal setting; flow rate nominal; temperature +12°C above baseline
- Root Cause Hypothesis: Thermal expansion of fluid reducing system compliance; pressure relief valve responding 0.8s later than design specification
- Supporting Logic: Pressure anomaly occurs before control response delay; temperature explains fluid property change; relief valve response time consistent with observed 0.8s lag
- Certification Action: Recommend thermal compensation validation in next test point; current behavior within acceptable margins for waiver support
Example 2: Instrumentation Failure vs. Real Event
Input: AOA (angle of attack) sensor reading shows impossibly rapid oscillations (±45° in 0.3 seconds) during steady cruise.
Output:
- Verdict: Instrumentation failure (sensor malfunction, not aerodynamic instability)
- Evidence: Adjacent pitot-static data stable; airspeed steady; pitch rate nominal; no corresponding g-load variations; oscillation frequency (33 Hz) matches known sensor resonance signature
- Confidence: High — multi-parameter consistency check eliminates aircraft dynamics as cause
- Impact: Data point excluded from test report; sensor flagged for replacement and trend monitoring
What's Included
- SKILL.md: Complete instruction set for flight test data analysis and anomaly detection
- Telemetry analysis template: Structured prompt format for importing test data and specifying analysis parameters
- Anomaly detection checklist: Statistical methods, conditional logic, and parameter relationship validation steps
- Root cause assessment framework: Evidence collection, hypothesis generation, and certification-ready documentation structure
- Parameter correlation matrix: Common flight test system interdependencies and expected cross-parameter relationships for baseline comparison
Who It's For
- Flight test engineers — analyzing instrumentation data and preparing test reports for certification
- Airworthiness specialists — generating technical assessments for FAA or military qualification memoranda
- Flight test pilots and chase engineers — understanding data anomalies during rapid post-flight debriefs
- Test instrumentation engineers — validating sensor health and detecting instrumentation failures
- Design engineers — interpreting flight test evidence to support design change justifications and risk mitigation plans
Best For
- Multi-parameter anomaly isolation across 50+ instrumentation channels
- Off-nominal vs. nominal test point comparison to identify variable causes
- Root cause hypothesis development for certification packages and technical memoranda
- Instrumentation validation and sensor failure detection
- Event timeline reconstruction and correlation analysis for complex multi-system interactions
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