IMU Diagnostics & Data Analysis
Diagnose IMU anomalies and validate sensor calibration instantly
What You Can Do
You can rapidly identify sensor faults, calibration mismatches, and performance degradation across gyroscope, accelerometer, and magnetometer axes. Claude analyzes raw IMU data logs, thermal drift patterns, and specification compliance to pinpoint root causes of inertial measurement failures and recommend precise corrections.
Features
Identifies offset errors and thermal drift across all IMU axes
Compares measured sensor behavior against factory specs and correction factors
Quantifies white noise, bias instability, and quantization effects
Detects sudden spikes, lockups, and coherent noise signatures
Identifies mechanical misalignment and sensor coupling issues
Confirms alignment with datasheet performance claims
Maps calibration drift across thermal operating range
Validates multi-sensor coherence and detects individual sensor failures
Example Output
Diagnostic Report: ICM-42688 Gyroscope Failure
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✅ Accelerometer Status: Nominal (bias < 15 mg, noise 8 mg/√Hz)
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❌ Gyroscope Status: Degraded
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Z-axis bias drift: +450°/h (spec: ±50°/h)
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Noise floor elevated: 0.08°/s/√Hz (spec: 0.015°/s/√Hz)
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Temperature coefficient: 0.9%/°C (indicates calibration table corruption)
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🔧 Recommendations:
- Verify calibration ROM integrity via factory reset sequence
- Recalibrate Z-axis gyroscope using precision rate table
- Reduce thermal cycling until firmware updated
Inertial Sensor Calibration Validation
Uploaded: accelerometer_log.csv | 10K samples @ 100 Hz
✓ Axis alignment error: <0.5° (within spec) ✓ Scale factor accuracy: 99.8% (excellent) ⚠ Bias stability: 12 mg over 1 hour (marginal — recommend post-calibration verification)
What's Included
- SKILL.md: Complete diagnostic framework with sensor models and analysis algorithms
- Calibration Checklist: Step-by-step sensor calibration validation workflow
- Data Analysis Templates: CSV parsers and statistical analysis templates for common IMU formats
- Fault Diagnosis Flowchart: Decision tree for identifying root causes (calibration vs. hardware vs. software)
- Specification Reference: Quick-lookup table for common IMU chips (MPU-6050, ICM-42688, BMI160, etc.)
- Example Data Logs: Sample sensor outputs (normal, degraded, failed states) for testing
- Thermal Calibration Worksheet: Temperature-dependent correction factor calculator
Who It's For
- Robotics engineers — Validate sensor health before production deployment
- Drone/UAV developers — Diagnose flight controller stability issues and calibration mismatches
- MEMS sensor specialists — Perform detailed inertial measurement characterization and validation
- Embedded systems engineers — Troubleshoot IMU integration problems and drift anomalies
- Hardware quality assurance — Detect manufacturing defects and specification violations early
Best For
- Sensor calibration validation and correction
- Anomaly detection and root cause diagnosis in inertial data
- Performance specification verification against datasheets
- Troubleshooting gyroscope/accelerometer drift and bias issues
- Pre-production sensor health validation and acceptance testing
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