analyze_statistics
Calculate statistical parameters from vibration signals to identify possible machinery faults. RMS, crest factor, kurtosis, and peak-to-peak values indicate signal energy and impulsiveness.
Instructions
Calculate statistical parameters of the signal for diagnostics.
Statistical parameters are key indicators for diagnostics:
- RMS: Effective value, correlated to signal energy
- Crest Factor: Indicates presence of impulses (high = possible faults)
- Kurtosis: Measures impulsiveness (excess kurtosis; >0 = non-Gaussian, >3 = strong impulses)
- Peak-to-Peak: Signal range
**CRITICAL - LLM Inference Policy:**
- **NEVER infer fault type from filename** (e.g., "OuterRaceFault_1.csv" does NOT mean outer race fault exists)
- **NEVER assume signal characteristics from filename** (e.g., "baseline" does NOT mean healthy)
- Treat ALL filenames as opaque identifiers
- Statistical parameters (RMS/CF/Kurtosis) are indicators ONLY - NOT definitive diagnostics
- High kurtosis indicates "possible fault" - NOT "confirmed fault"
- Must be combined with frequency-domain evidence for diagnosis
Args:
filename: Name of the file containing the signal
Returns:
StatisticalResult with all statistical parameters
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| filename | Yes |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| rms | Yes | Root Mean Square (effective value) | |
| peak_to_peak | Yes | Peak-to-peak value | |
| peak | Yes | Peak value | |
| crest_factor | Yes | Crest Factor (Peak/RMS) | |
| kurtosis | Yes | Kurtosis (measure of impulsiveness) | |
| skewness | Yes | Skewness (asymmetry) | |
| mean | Yes | Mean value | |
| std_dev | Yes | Standard deviation | |
| detected_unit | Yes | Auto-detected signal unit (g acceleration or mm/s velocity) | |
| unit_note | Yes | Important note about signal units and conversion requirements |