By LGDiMaggio
Perform industrial predictive maintenance on vibration signals: load and manage data, run quick health screenings, diagnose bearing/gear faults via FFT/envelope analysis, detect anomalies with ML models, assess ISO 20816-3 compliance/prognostics, and generate HTML/DOCX reports using autonomous agents, skills, and CLI commands on predictive-maintenance-mcp server.
Signal loading, generation, and cache management using the predictive-maintenance-mcp server. Use this skill when the user says "load signal", "import signal", "list signals", "available signals", "generate test signal", "create test data", "synthetic signal", "clear cache", "signal info", "what signals are loaded", "show signals", "signal formats", or needs help managing vibration signal files and the in-memory signal repository.
ML-based anomaly detection and model training for vibration signals using the predictive-maintenance-mcp server. Use this skill when the user says "anomaly detection", "train model", "detect anomalies", "outlier detection", "normal vs abnormal", "machine learning", "one-class SVM", "LOF", "local outlier factor", "train anomaly model", "predict anomalies", "PCA visualization", "clustering", or wants to build or use anomaly detection models on vibration data.
Complete bearing fault diagnostic workflow using vibration analysis and the predictive-maintenance-mcp server. Use this skill when the user says "diagnose bearing", "bearing fault", "bearing check", "detect bearing damage", "bearing vibration analysis", "inner race fault", "outer race fault", "ball defect", "cage fault", "BPFO", "BPFI", "BSF", "FTF", or asks to identify bearing problems from vibration data.
Search machine manuals, bearing catalogs, and technical documentation using RAG-based retrieval via the predictive-maintenance-mcp server. Use this skill when the user says "search documentation", "find in manual", "bearing catalog", "look up bearing", "machine manual", "extract specs", "find bearing specs", "SKF bearing", "technical documentation", "datasheet", "manual search", "what bearing", or needs to find technical information from stored documents.
Gear fault diagnosis workflow using vibration analysis via the predictive-maintenance-mcp server. Use this skill when the user says "gear fault", "gear diagnosis", "gear mesh", "gearbox analysis", "gear vibration", "tooth damage", "gear defect", "diagnose gear", "gear mesh frequency", "sideband analysis", or wants to detect gear faults from vibration signals.
Uses power tools
Uses Bash, Write, or Edit tools
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Give any AI assistant the ability to analyze vibration data, detect machinery faults, and generate professional diagnostic reports — through natural conversation.
An open-source MCP server and predictive maintenance AI agent that turns LLMs into condition monitoring assistants. Engineers describe what they need in plain language; the AI calls the right analysis tools and delivers results — bearing fault detection, risk assessment, anomaly detection, and remaining useful life estimation. Also available as a Claude Code plugin with 7 diagnostic skills. It's designed to support and accelerate expert decision-making.
pip install predictive-maintenance-mcp
Add to your Claude Desktop config (%APPDATA%\Claude\claude_desktop_config.json on Windows, ~/Library/Application Support/Claude/claude_desktop_config.json on macOS):
{
"mcpServers": {
"predictive-maintenance": {
"command": "predictive-maintenance-mcp"
}
}
}
Restart Claude Desktop. You're ready — try: "Load real_train/OuterRaceFault_1.csv and check if the bearing is healthy."
More options: install from source · VS Code setup · Docker / HTTPS deployment · use with local LLMs (Ollama)
Full diagnostic workflow: load signal → spectral analysis → fault detection → severity assessment → report generation
Upload a vibration signal → get a professional diagnosis through conversation.
| You say | The AI does |
|---|---|
| "Is this bearing healthy?" | Loads the signal, runs spectral analysis, checks for fault patterns, classifies severity |
| "Generate a full diagnostic report" | Produces an interactive HTML report with charts, fault markers, and severity assessment |
| "Extract specs from test_pump_manual.pdf and diagnose the signal" | Reads the equipment manual, looks up the bearing model, calculates expected fault frequencies, matches them against the signal |
| "Train an anomaly detector on my healthy baselines, then flag anomalies" | Trains a machine learning model on normal data, scores new signals, highlights outliers |
The AI doesn't guess — it calls 52 specialized MCP endpoints (46 tools, 2 resources, 4 prompts) running locally on your machine. Your data never leaves your infrastructure.
| Endpoint | Type | Description |
|---|---|---|
load_signal | Tool | Load vibration file (CSV, WAV, MAT, NPY, Parquet) |
list_signals | Tool | Browse available signal files with metadata |
list_stored_signals | Tool | List cached signals in memory |
get_signal_info | Tool | Signal metadata (sampling rate, duration, stats) |
generate_test_signal | Tool | Create synthetic signals for testing |
clear_signal / clear_all_signals | Tool | Cache management |
signal://list | Resource | Browse all signal files |
signal://read/{filename} | Resource | Read signal metadata |
| Tool | Description |
|---|---|
analyze_fft | Frequency spectrum with automatic peak detection |
analyze_envelope | Envelope analysis for bearing fault detection |
analyze_statistics | Time-domain features (RMS, kurtosis, crest factor) |
compute_power_spectral_density | Power spectral density (Welch method) |
compute_spectrogram_stft | Time-frequency spectrogram |
extract_features_from_signal | 17+ statistical and spectral features |
compute_envelope_spectrum_tool | Envelope spectrum computation |
plot_signal / plot_spectrum / plot_envelope | Visualization tools (3 tools) |
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