mcp-server-mcsa

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.

mcp-server-mcsa turns any LLM into a predictive-maintenance expert. By integrating advanced techniques such as Fast Fourier Transform (FFT) and envelope analysis, the system can listen to a motor's electrical signature and automatically identify mechanical and electrical anomalies — all through natural language.

MCSA is an industry-standard condition-monitoring technique that analyses the harmonic content of the stator current to detect rotor, stator, bearing, and air-gap faults in electric motors — without requiring vibration sensors, downtime, or physical access to the machine. This server brings the full MCSA diagnostic workflow to any MCP-compatible AI assistant (Claude Desktop, VS Code Copilot, and others), enabling both interactive expert analysis and automated condition-monitoring pipelines.

Features

• Real signal loading — read measured data from CSV, TSV, WAV, and NumPy .npy files
• Motor parameter calculation — slip, synchronous speed, rotor frequency from nameplate data
• Fault frequency computation — broken rotor bars, eccentricity, stator faults, mixed eccentricity
• Bearing defect frequencies — BPFO, BPFI, BSF, FTF from bearing geometry
• Signal preprocessing — DC removal, normalisation, windowing, bandpass/notch filtering
• Spectral analysis — FFT spectrum, Welch PSD, spectral peak detection
• Envelope analysis — Hilbert-transform demodulation for mechanical/bearing faults
• Time-frequency analysis — STFT with frequency tracking for non-stationary conditions
• Fault detection — automated severity classification (healthy / incipient / moderate / severe)
• One-shot diagnostics — full pipeline from signal array or directly from file
• Test signal generation — synthetic signals with configurable fault injection for demos and benchmarking

Tools (19)

Resources

Prompts

Installation

Using uv (recommended)

uvx mcp-server-mcsa

Using pip

pip install mcp-server-mcsa

From source

git clone https://github.com/LGDiMaggio/mcp-motor-current-signature-analysis.git
cd mcp-motor-current-signature-analysis
pip install -e .

Configuration

Claude Desktop

Add to your Claude Desktop configuration file:

{
  "mcpServers": {
    "mcsa": {
      "command": "uvx",
      "args": ["mcp-server-mcsa"]
    }
  }
}

{
  "mcpServers": {
    "mcsa": {
      "command": "python",
      "args": ["-m", "mcp_server_mcsa"]
    }
  }
}

VS Code

Add to .vscode/mcp.json in your workspace:

{
  "servers": {
    "mcsa": {
      "command": "uvx",
      "args": ["mcp-server-mcsa"]
    }
  }
}

Or with pip:

{
  "servers": {
    "mcsa": {
      "command": "python",
      "args": ["-m", "mcp_server_mcsa"]
    }
  }
}

Usage Examples

Real Signal — One-Shot Diagnosis

The fastest way to analyse a measured signal is the diagnose_from_file tool. Simply provide the file path and motor nameplate data:

"Diagnose the motor from C:\data\motor_phaseA.csv — 50 Hz supply, 4 poles, 1470 RPM"

The server loads the file, preprocesses the signal, computes the spectrum, runs all fault detectors, and returns a complete JSON report with severity-classified results.

Step-by-Step Workflow

1. Load a measured signal (or generate a synthetic one):

   "Load the signal from measurement.wav"
   or: "Generate a test signal with a broken-rotor-bar fault"

2. Calculate motor parameters:

   "Calculate motor parameters for a 4-pole motor, 50 Hz supply, running at 1470 RPM"

3. Compute expected fault frequencies:

   "What are the expected fault frequencies for this motor?"

4. Analyse the spectrum:

   "Compute the FFT spectrum of this signal"

5. Detect specific faults:

   "Check for broken rotor bars in this spectrum"

6. Envelope analysis (optional):

   "Compute the envelope spectrum to check for bearing modulation"

Quick Diagnosis from Signal Array

The run_full_diagnosis tool runs the entire pipeline on a signal already in memory in a single call:

Input: raw signal array + motor nameplate data
Output: complete report with fault severities and recommendations

Bearing Analysis

For bearing fault analysis, you need the bearing geometry (number of balls, ball diameter, pitch diameter, contact angle). The server will:

1. Calculate characteristic defect frequencies (BPFO, BPFI, BSF, FTF)
2. Compute expected current sidebands
3. Search the spectrum for those sidebands

Supported File Formats

Fault Detection Theory

Broken Rotor Bars (BRB)

Sidebands at $(1 \pm 2s) \cdot f_s$ where $s$ is slip and $f_s$ is supply frequency. Severity is classified by the dB ratio of sideband to fundamental amplitude.

Eccentricity

Sidebands at $f_s \pm k \cdot f_r$ where $f_r$ is the rotor mechanical frequency.

Stator Inter-Turn Faults

Sidebands at $f_s \pm 2k \cdot f_r$ due to winding asymmetry.

Bearing Defects

Torque oscillations modulate the stator current, creating sidebands at $f_s \pm k \cdot f_{defect}$. Defect frequencies depend on bearing geometry (BPFO, BPFI, BSF, FTF).

Severity Thresholds (dB below fundamental)

Note: These are general guidelines. Actual thresholds should be adapted to the specific motor, load, and application based on baseline measurements.

Development

Setup

git clone https://github.com/LGDiMaggio/mcp-motor-current-signature-analysis.git
cd mcp-motor-current-signature-analysis
uv sync --dev

Run tests

uv run pytest

Run with MCP Inspector

uv run mcp dev src/mcp_server_mcsa/server.py

Lint and type check

uv run ruff check src/ tests/
uv run pyright src/

Dependencies

• mcp — Model Context Protocol SDK
• numpy — numerical computing
• scipy — signal processing (FFT, filtering, Hilbert transform)
• pydantic — data validation

Documentation

For a detailed reference of every tool, resource, and prompt — including parameter tables, diagnostic workflows, integration patterns, and severity thresholds — see the Usage Guide.

License

MIT — see LICENSE for details.