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AI-Powered Predictive Maintenance & Fault Diagnosis through Model Context Protocol. An open-source framework for integrating Large Language Models with predictive maintenance and fault diagnosis workflows.
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Description
Train novelty detection on healthy baselines
2D/3D anomaly projection
Custom threshold-based vibration alerting
Example
Baseline deviation detection for early degradation warning
ISO-compliant diagnostic report generation
Read signal metadata
Envelope spectrum computation
Health classification
Structured Word document report
ISO 10816 vibration severity alert classification (zones A/B/C/D)
Type
Tool
Frequency spectrum with automatic peak detection
Compute expected fault frequencies from bearing geometry
Detect peaks at fault frequencies
Score new signals for anomalies
Tool
Power spectral density (Welch method)
Look up bearing specs by model number
Interactive frequency analysis report
Trend detection on feature time series (increasing/decreasing/stable)
Fast health screening
Tool
Time-domain features (RMS, kurtosis, crest factor)
Integrated evidence-based diagnosis pipeline
Remaining Useful Life estimation (linear, exponential, Weibull, Kalman)
Tool
Time-frequency spectrogram
Catalog lookup + frequency calculation
Envelope analysis with fault markers
Tool
Envelope analysis for bearing fault detection
Multi-fault detection (inner/outer/ball/cage)
Browse available documentation
Complete bearing fault diagnostic decision tree
17+ statistical and spectral features
Vibration severity assessment (4 severity zones)
Severity zone visualization
What it does
ISO 20816 severity zone chart
Description
Context-aware maintenance recommendations from diagnosis
For
What it does
Semantic search over equipment manuals
Cross-signal feature comparison
Gear fault detection workflow