matlab-extract-rotating-machinery-features
Extract features from signals collected on rotating machinery components, including motors, pumps, fans, gears, bearings, and shafts. Signals can include vibration, electrical, or environmental sensor measurements. Use when developing and deploying condition monitoring and fault detection applications for rotating machinery, including industrial machines, electrical vehicles, internal combustion engines, turbines, and drive trains.
npx skills add matlab/matlab-agentic-toolkit --skill matlab-extract-rotating-machinery-features --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Rotating Machinery Feature Extraction Extract predictive features from rotating machinery data for condition monitoring and fault detection applications. This skill covers the essential feature extraction workflow steps and algorithms specialized to rotating machinery. ## When to Use - User has uniformly-sampled time series data representing rotating machinery sensor measurements stored as matrix, `timetable`, or cell array variables. - User is building a condition monitoring or fault detection system for rotating machinery. - User wants to process rotating machinery data for feature extraction. - User wants to select most predictive features and construct a health indicator from features. - User is deploying condition monitoring and fault detection systems. - User has high-frequency vibration data and wants feature-based data reduction for efficient condition monitoring. ## When NOT to Use - User has non-time-series data such as images, videos, or tabular (unordered) data. - User wants fault classification (as opposed to fault detection) with two or more fault classes. Use `classificationLearner` instead. - User has time series data (labeled or unlabeled) and wants to build anom
- When to Use
- When NOT to Use
- Feature Extraction Workflow
- Step 1: Process data for feature extraction
- Step 2: Extract features from data
- Extracting features across many files (batch processing)
- Signal type: vibration vs. electrical vs. other
- Step 3: Rank and select features
- Step 4: Develop a health indicator using selected features
- Step 5: Deploy the application
- Specialized techniques
What does the matlab-extract-rotating-machinery-features skill do?
Extract features from signals collected on rotating machinery components, including motors, pumps, fans, gears, bearings, and shafts. Signals can include vibration, electrical, or environmental sensor measurements. Use when developing and deploying condition monitoring and fault detection applications for rotating machinery, including industrial machines, electrical vehicles, internal combustion engines, turbines, and drive trains.
How do I install it?
Run `npx skills add matlab/matlab-agentic-toolkit --skill matlab-extract-rotating-machinery-features --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From matlab/matlab-agentic-toolkit, a repository with 868 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.