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 anomaly detection models. Use timeSeriesAnomalyDetector instead.
• User has very low-frequency data not suitable for statistical or spectral feature extraction.

Feature Extraction Workflow

Follow these 5 steps interactively:

Task Progress:
- [ ] Step 1: Process data for feature extraction
- [ ] Step 2: Extract features from data
- [ ] Step 3: Rank and select features
- [ ] Step 4: Develop a health indicator using selected features
- [ ] Step 5: Deploy the application

Do NOT silently choose parameters or make assumptions. Engage the user at each decision point. In particular, ask the user to provide important system parameters such as

• Bearing and gear geometry parameters
• Rotation speeds (RPMs)
• Structural resonance frequencies, if known