extract_features_from_signal
Extract 17 statistical features from vibration signal segments using overlapping windows for machinery health analysis.
Instructions
Extract time-domain features from signal using sliding window segmentation.
Segments the signal into overlapping windows and extracts 17 statistical features
from each segment. Features include: mean, std, RMS, kurtosis, crest factor, entropy, etc.
Args:
signal_file: Name of the CSV file in data/signals/
sampling_rate: Sampling frequency in Hz (auto-detect from metadata if None)
segment_duration: Duration of each segment in seconds (default: 0.1)
overlap_ratio: Overlap between segments, 0-1 (default: 0.5 = 50%)
ctx: MCP context for progress/logging
Returns:
FeatureExtractionResult with features matrix and metadata
Example:
extract_features_from_signal(
"healthy_motor.csv",
sampling_rate=10000,
segment_duration=0.2,
overlap_ratio=0.5
)
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| signal_file | Yes | ||
| sampling_rate | No | ||
| segment_duration | No | ||
| overlap_ratio | No |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| num_segments | Yes | Number of segments extracted | |
| segment_length_samples | Yes | Samples per segment | |
| segment_duration_s | Yes | Duration of each segment in seconds | |
| overlap_ratio | Yes | Overlap ratio between segments | |
| features_shape | Yes | Shape of feature matrix [num_segments, num_features] | |
| feature_names | Yes | Names of extracted features | |
| features_preview | Yes | First 5 segments features (preview) |