MCP Motor Current Signature Analysis

@LGDiMaggio

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基本信息

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其他

传输方式

stdio

作者

LGDiMaggio

提交者

Luigi Di Maggio

关于 MCP Motor Current Signature Analysis

暂无概览

MCP Motor Current Signature Analysis 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcp-motor-current-signature-analysis": {
      "command": "uvx",
      "args": [
        "mcp-server-mcsa",
        "--help"
      ]
    }
  }
}

MCP Motor Current Signature Analysis 工具

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当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。

概览

What is Mcp Motor Current Signature Analysis?

A Model Context Protocol (MCP) server for Motor Current Signature Analysis (MCSA) — non-invasive spectral analysis and fault detection in electric motors using stator-current signals. It turns any MCP-compatible LLM into a predictive-maintenance expert by automatically identifying mechanical and electrical anomalies from current data.

How to use Mcp Motor Current Signature Analysis?

Install with uvx (recommended) or pip, then add the server configuration to your MCP client (Claude Desktop, VS Code, Cursor). Use natural-language commands to load signals, compute parameters, and run diagnostics — for example, "Generate a test signal with a broken rotor bar fault and run a full diagnosis." The server provides 21 tools, including load_signal_from_file, run_full_diagnosis, diagnose_from_file, and generate_test_current_signal.

Key features of Mcp Motor Current Signature Analysis

Real signal loading from CSV, WAV, and NumPy files
Fault frequency computation for rotor, stator, and bearing defects
Signal preprocessing: filtering, normalisation, windowing
Spectral analysis (FFT, Welch PSD, envelope spectrum)
Automated fault detection with severity classification (healthy/incipient/moderate/severe)
Persistent data store in ~/.mcsa_data/ with short IDs

Use cases of Mcp Motor Current Signature Analysis

Diagnose a real motor from a current signal file in one step
Step-by-step analysis: load, preprocess, compute spectrum, detect faults
Generate synthetic test signals with configurable faults for benchmarking
Analyse bearing defects using geometry (BPFO, BPFI, BSF, FTF)
Integrate into automated condition-monitoring pipelines via MCP-compatible assistants

常见问题

What Python version and dependencies are required?

Python 3.10+ is required. The recommended installation method uses `uvx`, which handles Python and packages automatically. Alternatively, install via `pip install mcp-server-mcsa`.

Where does the server store data?

Signals and spectra are persisted as compressed `.npz` files in `~/.mcsa_data/` (configurable via the `MCSA_DATA_DIR` environment variable). Data survives server restarts and is referenced by short IDs to keep large arrays out of chat context.

What file formats are supported for loading signals?

CSV/TSV (with time column or user-supplied rate), WAV (rate from header), and NumPy `.npy` files are supported.

What faults can the server detect?

Broken rotor bars (via sidebands at (1±2s)·fs), air-gap eccentricity, stator inter-turn short circuits, and bearing defects. Severity is classified automatically.

How do I troubleshoot if the server disconnects?

Check the logs at `%APPDATA%\Claude\logs\` (Windows) or `~/Library/Logs/Claude/` (macOS). Most common cause is a missing command in PATH; using `uvx` avoids these issues. Restart the terminal after installing uv and restart the MCP client after editing configuration files.

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