Predictive Maintenance MCP

Predictive Maintenance MCP Server

Give your AI assistant evidence-based vibration diagnostics — machinery fault detection, ISO-cited severity, and diagnostic reports built to support and accelerate expert decision-making.

An open-source MCP server that turns LLMs into condition monitoring assistants for reliability engineers. Its core design rule: the server refuses to guess. No diagnosis is ever inferred from filenames or statistical parameters alone — a fault indication requires matching spectral evidence. Every severity claim cites ISO 20816-3, and the evaluative wording in reports is authored by the server, not improvised by the model. The AI orchestrates the analysis and presents the evidence — detected fault frequencies, matched fault patterns, severity zones — while the final judgment stays with the engineer. Also available as a Claude Code plugin with 8 diagnostic skills.

See It in Action

Full diagnostic workflow: load signal → spectral analysis → fault detection → severity assessment → report generation

Choose Your Path

What Can It Do?

Point the AI at a vibration signal → get the evidence behind the fault — detected frequencies, matched fault patterns, ISO-cited severity — to support your call.

The AI doesn't guess: it calls 41 specialized MCP endpoints (38 tools + 3 prompts) running locally on your machine. Every signal is referenced by a single signal_id handle from load to report. Your data never leaves your infrastructure.

Full endpoint reference, grouped by category: Tool Catalog.

Quick Start

One route, from a fresh clone to a first diagnosis in Claude Desktop. You need Git (or the repository ZIP from GitHub, extracted), uv (it fetches Python 3.11 by itself) and Claude Desktop.

1. Get the repository. The 20 sample signals ship with it, not with the PyPI package: real bearing measurements from the MathWorks rolling-element bearing dataset, healthy baselines plus inner and outer race faults, each with its companion metadata (data/README.md).

git clone https://github.com/LGDiMaggio/predictive-maintenance-mcp.git
cd predictive-maintenance-mcp

2. Install and connect. On Windows, one script creates the environment, installs the server and writes the Claude Desktop entry (clones inside OneDrive or other synced folders are handled):

powershell -ExecutionPolicy Bypass -File setup_claude.ps1

uv venv .venv --python 3.11
uv pip install --python .venv/bin/python .

{
  "mcpServers": {
    "predictive-maintenance": {
      "command": "/path/to/predictive-maintenance-mcp/.venv/bin/python",
      "args": ["-m", "predictive_maintenance_mcp"],
      "env": { "PDM_PROJECT_DIR": "/path/to/predictive-maintenance-mcp" }
    }
  }
}

3. Restart and check. Quit Claude Desktop completely (File, then Quit) and start it again. In a new chat ask:

"List the available vibration signals."

The 20 included signals should appear. If they do not, see INSTALL.md, Troubleshooting.

4. First diagnosis. Try:

"Load real_train/OuterRaceFault_1.csv. The shaft runs at 1500 rpm: check the bearing against the fault frequencies declared in its metadata, assess the ISO severity and generate the envelope report."

What comes back is the server's evidence, not the model's opinion: two of the four declared frequencies come back as high evidence in the envelope spectrum, the outer race ranked most likely and the inner race graded high as well because its second harmonic shares a peak with the third outer-race harmonic, while the ball and cage frequencies stay unmatched; the ISO 20816-3 severity zone of the broadband velocity, with the machine group and support type the assessment assumed; and the path of an HTML report in reports/ with those frequencies marked on the envelope spectrum. The call on the machine stays with you.

The file name carries the fault label, but the server never reads names. Ask which frequencies matched and with what deviation: the answer rests on the spectrum alone.

Use your own data. Put a single-column CSV without a header row where the server can read it (a folder of yours under data/signals/, or an absolute path), add a <name>_metadata.json next to it with sampling_rate and signal_unit (for example "g"), and ask for the analysis stating the shaft speed. Details and traps: Use Your Own Data.

Other clients: Claude Code, VS Code, Cursor and any other MCP client: INSTALL.md, Manual Setup.

Benchmark

A blind, reproducible diagnostic-accuracy benchmark on the public CWRU Bearing Data Center dataset (12 kHz drive-end subset: 60 fault records + 4 normal baselines). Fault labels never reach the system under test — signals enter under opaque ids, a separate scorer is the only label reader, and blindness, checksum integrity, and determinism are enforced by CI-run guard tests, not prose. Results are stratified by the per-record diagnosability grades of the Smith & Randall (2015) reference study, so records that study found undiagnosable by any classical method are reported separately instead of inflating or deflating the headline.

On records the reference study grades clearly diagnosable (Y1+Y2, 44 records): characteristic fault frequency detected on 44/44, correct fault ranked first on 34/44 (77.3%), and 9/9 on the textbook-signature (Y1) stratum. On the 4 healthy baselines, 2 records raised a false indication under the same criterion.

The numbers above are read from the committed, re-runnable artifact (results.json) and drift-guarded by CI: every value is bound to its key in the artifact, and a mismatch fails the build. Methodology, blind protocol, and honest-benchmarking notes: docs/benchmark-methodology.md. Reproduce with:

python -m benchmarks.cwru all

Claude Code Plugin

The project includes a plugin for Claude Code with domain-specific skills that activate automatically during conversation.

/plugin marketplace add LGDiMaggio/predictive-maintenance-mcp
/plugin install predictive-maintenance@predictive-maintenance-marketplace

The plugin adds 8 skills that activate automatically based on context (bearing-diagnosis, gear-diagnosis, quick-screening, report-generation, anomaly-detection, signal-management, documentation-search, prognostics), 2 agents that run multi-step diagnostic workflows end-to-end and hand you the evidence (diagnostic-pipeline, signal-explorer), and 3 commands for quick entry points (/pm-diagnose, /pm-screen, /pm-report).

Full skill, agent, and command reference: Plugin README.

Reports

All analysis tools generate interactive HTML reports you can open in any browser — pan, zoom, hover for details. Also supports structured Word (.docx) exports.

Asset Health Ledger

A single diagnosis answers "what does this signal show". The asset health ledger answers "what changed on this machine since the reference": every measurement loaded with a declared identity (asset, measurement point, acquisition instant, operating conditions) is recorded in a local append-only ledger with its derived indicators, compared with the healthy baseline the engineer declares, and judged against the reference band with the rule it applied stated in the result. Four tools carry the workflow: declare_measurement_point, declare_healthy_baseline, get_asset_history and assess_asset_change. Contract and operations: Adapter Guide · worked flow: Examples · reference adapter: STWIN.box.

Architecture

          YOU (natural language)
               │
               v
     LLM (Claude, GPT, Ollama...)
     understands intent, selects tools
               │
               v  ── Model Context Protocol ──
    ┌──────────────────────────────┐
    │    Predictive Maintenance    │
    │         MCP Server           │
    │                              │
    │  Signal Analysis    Reports  │
    │  Fault Detection    ML       │
    │  Severity Rating    RAG Docs │
    └──────────────────────────────┘
               │
               v
       YOUR DATA (stays local)
    signals · manuals · models

The codebase follows a modular architecture organized around the ISO 13374 Six-Block Diagnostic standard — signal acquisition, processing, diagnostics, prognostics, and decision support as separate sub-packages. Standards implemented: ISO 13374, ISO 20816-3, MIMOSA OSA-CBM. Module-level detail: Architecture guide.

Key design choices:

• Privacy-first — raw vibration data never leaves your machine; only computed results flow to the LLM
• LLM-agnostic — works with Claude, ChatGPT, Microsoft Copilot Studio, or any MCP-compatible client. Use Ollama for fully air-gapped deployments
• Modular — use only the tools you need, extend with your own

Documentation

Testing

85%+ test coverage, enforced as a CI minimum, across Windows, macOS, and Linux (Python 3.11 & 3.12) — the current measured figure is on the codecov badge above.

pytest                                  # run all tests
pytest --cov=src --cov-report=html      # with coverage report

20+ test files covering signal analysis, fault detection, severity assessment, ML models, report generation, RAG search, and real bearing fault data validation.

Roadmap

• 41 MCP endpoints (38 tools, 3 prompts) with modular architecture and a single signal_id handle
• Claude Code plugin (8 skills, 2 agents, 3 commands)
• 85%+ test coverage enforced in CI, CI/CD on 3 platforms
• Docker + SSE/HTTP transport for enterprise deployment
• Semantic document search (FAISS + TF-IDF)
• Blind, reproducible diagnostic benchmark on the CWRU dataset (extensible to Paderborn)
• Customizable severity thresholds
• Remaining useful life (RUL) estimation from repeated measurements (linear, exponential, Kalman)
• Trend analysis and degradation onset detection
• Asset health ledger: per-asset measurement history with a declared reference and qualified comparability
• Multi-signal trending and historical comparison
• Real-time streaming (MQTT/Kafka)
• Fleet dashboard for multi-asset monitoring
• CMMS integration (SAP, Maximo, Infor)

Ideas? Open a discussion or create an issue.

Are you using this?

I'd genuinely love to know. Whether you ran it on real machinery or just tried the sample data, drop a line in Discussions — one sentence about your machine or use case is enough. Real-world feedback directly shapes what gets built next.

Related

claude-stwinbox-diagnostics — Extends this project by connecting a physical edge sensor (STEVAL-STWINBX1) to Claude via MCP, with Claude Skills for guided condition monitoring. Same analysis engine, real hardware, operator-friendly reports.

Contributing

Contributions welcome from everyone — not just programmers. Domain experts, technical writers, and testers are equally valued. See CONTRIBUTING.md for paths tailored to your background.

Quick start: browse Issues for good first issue or help wanted labels.

Citation

@software{dimaggio_predictive_maintenance_mcp_2025,
  title   = {Predictive Maintenance MCP Server},
  author  = {Di Maggio, Luigi Gianpio},
  year    = {2025},
  version = {0.13.0},
  url     = {https://github.com/LGDiMaggio/predictive-maintenance-mcp},
  doi     = {10.5281/zenodo.17611542}
}

License

MIT — see LICENSE. Sample data is CC BY-NC-SA 4.0 (non-commercial); for commercial use, replace with your own machinery data.

Acknowledgments

MCP Python SDK (descended from FastMCP) · Model Context Protocol by Anthropic · Sample data from MathWorks · Core development assisted by Claude

An open-source predictive maintenance AI agent and condition monitoring copilot — built to support reliability engineers and the developer community.