An AI copilot for maintenance teams. Upload vibration data, ask questions in plain language, get clear diagnoses, risk levels, and actionable reports — without writing a single line of code.
pip install predictive-maintenance-mcp
Without dedicated diagnostic tools, an AI can only speculate. With them, it runs a real analysis pipeline and delivers measured, repeatable answers.
“It might be a bearing fault… maybe check the alignment? I’d suggest monitoring it.”
“Outer-race bearing wear detected at 89.4 Hz (expected 89.8 Hz). Vibration: 4.2 mm/s — alert zone. Recommend inspection within 24 h.”
Same AI, different outcomes. The tools make the difference.
The assistant runs the full analysis pipeline on your machine and explains results in language your team understands.
pip install predictive-maintenance-mcp — connect to Claude Desktop, VS Code, or any MCP client.
“Load OuterRaceFault_1.csv and check if the bearing is healthy.”
Get a clear diagnosis, confidence signals, risk classification, and recommended next steps.
reports/.Watch the full diagnostic flow — from raw signal to professional report — in Claude Desktop and Claude Code.
Load signal → spectral analysis → fault detection → severity assessment → report
Domain skills activate automatically · slash commands for quick diagnostics
From raw vibration files to maintenance decisions — all running locally on your infrastructure.
Decompose signals into frequency components with automatic peak detection, harmonic identification, and clear visualizations.
Highlight subtle impact patterns that indicate early bearing or gear wear — even when the raw signal looks normal.
Automatic severity zones so teams know immediately what needs urgent attention and what can wait.
Train on healthy baselines, then automatically flag unusual signals. Machine learning models with zero-code setup.
Generate HTML reports with interactive charts and structured Word documents. Designed for engineers, operators, and management.
Search equipment manuals and bearing catalogs. Extract specs from PDFs and cross-check against measured data automatically.
Load vibration data from CSV, WAV, MAT, NPY, and Parquet files. Automatic sampling rate detection and metadata extraction.
Evidence-based maintenance suggestions with confidence levels. No guessing — every recommendation cites its source data.
Plugin architecture for custom signal processors and report formats. Works with Claude, ChatGPT, Ollama, or any MCP-compatible client.
7 skills, 2 autonomous agents, 3 slash commands — works inside Claude Code.
/plugin marketplace add LGDiMaggio/predictive-maintenance-mcp
Bearing diagnosis, gear analysis, quick screening, report generation, anomaly detection, signal management, and documentation search.
diagnostic-pipeline: End-to-end workflow from signal to report. signal-explorer: Compare and characterize multiple signals.
/pm-diagnose for bearing diagnosis, /pm-screen for quick health checks, /pm-report for instant reports.
Raw vibration files stay local. Only computed results (severity scores, fault types) flow to the AI for interpretation.
Raw vibration data is processed entirely on your machine. Only computed results flow to the LLM — never raw waveforms.
Claude, ChatGPT, Microsoft Copilot Studio, or Ollama for fully air-gapped deployments. Any MCP client works.
Six-stage diagnostic flow: ingest → process → detect → assess → predict → recommend. Each stage is independent and testable.
Use only the tools you need. Plugin architecture for custom signal processors, diagnostics, and report formats.
Whether you maintain machines or build AI tools — this is your starting point.
Diagnose machine health through conversation. No signal-processing expertise required.
Study real MCP tool architecture and build industrial copilots on a production-ready foundation.
20 vibration signals from production machinery — install and start diagnosing immediately.
Training set: 2 healthy baselines + 12 fault signals (inner race, outer race). Test set: 1 healthy baseline + 5 fault signals.
“Load OuterRaceFault_1.csv and diagnose the bearing.”