predictive-maintenance-mcp MCP Server
LGDiMaggio/predictive-maintenance-mcp
[](https://agentrank-ai.com/tool/LGDiMaggio--predictive-maintenance-mcp/?utm_source=badge&utm_medium=readme&utm_campaign=agentrank_badge) MCP server for predictive maintenance and machinery fault diagnosis. Gives AI assistants evidence-based vibration analysis - FFT, envelope, bearing fault detection, ISO 20816-3 severity - with a measured, blind CWRU benchmark. Local-first: raw signals never leave your machine. Includes a Claude Code plugin.
claude mcp add agentrank -- npx -y agentrank-mcp-server Overview
LGDiMaggio/predictive-maintenance-mcp is a Python MCP server. MCP server for predictive maintenance and machinery fault diagnosis. Gives AI assistants evidence-based vibration analysis - FFT, envelope, bearing fault detection, ISO 20816-3 severity - with a measured, blind CWRU benchmark. Local-first: raw signals never leave your machine. Includes a Claude Code plugin. Topics: anomaly-detection, bearing-fault-diagnosis, claude-agents, claude-ai, condition-monitoring, envelope-analysis, fastmcp, fault-diagnosis, iiot, industrial-iot, industry-4-0, mcp-server, model-context-protocol, predictive-analytics, predictive-maintenance, signal-processing, vibration-analysis, ai-agent, iso-20816, mcp.
Ranked #8092 out of 24846 indexed tools.
Ecosystem
Score Breakdown
101 stars → growing community
Last commit 1mo ago → moderate activity
6/20 issues closed → many open issues
4 contributors → small team
No dependents → no downstream usage
60 weekly installs → early adoption
Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →
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From the README
# 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](https://modelcontextprotocol.io/) 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 <p align="center"> </p>Read full README on GitHub →
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