How do I install Motor Current Signature Analysis?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Motor Current Signature Analysis need an API key?

No required environment variable is listed on this record.

What transport does Motor Current Signature Analysis use?

Motor Current Signature Analysis runs locally over stdio.

When was Motor Current Signature Analysis last checked?

Last checked 2026-11-09. MCP server for Motor Current Signature Analysis — fault detection in electric motors

Motor Current Signature Analysis MCP server

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
Persistent data store — signals and spectra saved to ~/.mcsa_data/ as compressed .npz files; referenced by short IDs (sig_xxxx, spec_xxxx) to keep large arrays out of the chat context; data survives server restarts

Tools (21)

Tool	Description
`inspect_signal_file`	Inspect a signal file format and metadata without loading
`load_signal_from_file`	Load a current signal from CSV / WAV / NPY file → returns `signal_id`
`calculate_motor_params`	Compute slip, sync speed, rotor frequency from motor data
`compute_fault_frequencies`	Calculate expected fault frequencies for all common fault types
`compute_bearing_frequencies`	Calculate BPFO, BPFI, BSF, FTF from bearing geometry
`preprocess_signal`	DC removal, filtering, normalisation, windowing pipeline → returns new `signal_id`
`compute_spectrum`	Single-sided FFT amplitude spectrum → returns `spectrum_id`
`compute_power_spectral_density`	Welch PSD estimation → returns `spectrum_id`
`find_spectrum_peaks`	Detect and characterise peaks in a spectrum
`detect_broken_rotor_bars`	BRB fault index with severity classification
`detect_eccentricity`	Air-gap eccentricity detection via sidebands
`detect_stator_faults`	Stator inter-turn short circuit detection
`detect_bearing_faults`	Bearing defect detection from current spectrum
`compute_envelope_spectrum`	Hilbert envelope spectrum for modulation analysis
`compute_band_energy`	Integrated spectral energy in a frequency band
`compute_time_frequency`	STFT analysis with optional frequency tracking
`generate_test_current_signal`	Synthetic motor current with optional faults → returns `signal_id`
`run_full_diagnosis`	Complete MCSA diagnostic pipeline from signal or `signal_id`

Resources

URI	Description
`mcsa://fault-signatures`	Reference table of fault signatures, frequencies, and empirical thresholds

Prompts

Prompt	Description
`analyze_motor_current`	Step-by-step guided workflow for MCSA analysis

Installation & Setup

Step 1 — Install uv (one-time, if you don't have it)

uv is the recommended Python package manager. It handles everything (Python, packages, virtual environments) in a single tool and is used throughout the MCP ecosystem.

Windows (PowerShell):

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

macOS / Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

After installing, restart your terminal so the uv / uvx commands are available.

Step 2 — Verify it works

uvx mcp-server-mcsa --help

You should see the help text. That's it — no pip install needed. uvx downloads and runs the package automatically in an isolated environment.

Step 3 — Add to your MCP client

Pick your client and add the configuration below. No other steps are required.

Claude Desktop

Open the config file:

Windows: %APPDATA%\Claude\claude_desktop_config.json
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

Add mcsa inside the mcpServers object (create the file if it doesn't exist):

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

Then restart Claude Desktop.

VS Code (Copilot / Continue)

Create (or edit) .vscode/mcp.json in your workspace:

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

Cursor

Go to Settings → MCP Servers → Add new server:

Type: command
Command: uvx mcp-server-mcsa

Step 4 — Test

In your MCP client, try:

"Generate a test signal with a broken rotor bar fault and run a full diagnosis. Motor: 4 poles, 50 Hz, 1470 RPM."

If the server responds with a diagnostic report, you're all set.

pip install mcp-server-mcsa

Then configure your client with:

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

⚠️ Common issue on Windows: if you installed Python from the Microsoft Store, the mcp-server-mcsa command may not be in your PATH, causing a "server disconnected" error. In that case, find your Python path with python -c "import sys; print(sys.executable)" and use the full path in the config:
{
  "mcpServers": {
    "mcsa": {
      "command": "C:/Users/YOU/AppData/Local/.../python.exe",
      "args": ["-m", "mcp_server_mcsa"]
    }
  }
}
Using uvx avoids this problem entirely.

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

Configure the client to point to the local repo:

{
  "mcpServers": {
    "mcsa": {
      "command": "uv",
      "args": ["--directory", "/absolute/path/to/mcp-motor-current-signature-analysis", "run", "mcp-server-mcsa"]
    }
  }
}

Run tests:

uv run pytest

Debug with MCP Inspector:

uv run mcp dev src/mcp_server_mcsa/server.py

Troubleshooting

Problem	Fix
"server disconnected" on Claude Desktop	Check the logs at `%APPDATA%\Claude\logs\` (Windows) or `~/Library/Logs/Claude/` (macOS). Most common cause: the command in the config is not found. Use `uvx` to avoid PATH issues.
`uvx: command not found`	Restart your terminal after installing uv. On Windows, you may need to close and reopen PowerShell.
`mcp-server-mcsa: command not found` (pip)	The script wasn't added to PATH. Use `python -m mcp_server_mcsa` instead, or switch to `uvx`.
Server starts but tools don't appear	Make sure you restarted the MCP client after editing the config.

Data Store

Signals and spectra are persisted to disk as compressed .npz files in ~/.mcsa_data/ (configurable via the MCSA_DATA_DIR environment variable). This means:

Large arrays never enter the chat — only short IDs (sig_xxxx, spec_xxxx) and compact summaries are returned to the LLM.
Data survives server restarts — reopen Claude Desktop tomorrow and your signals are still there.
All data in one place — loaded measurements and generated test signals live side by side in the same folder.

~/.mcsa_data/
  signals/
    sig_a1b2c3d4.npz   ← loaded from CSV
    sig_e5f6g7h8.npz   ← generated test signal
  spectra/
    spec_i9j0k1l2.npz  ← FFT result

Use list_stored_data to see everything on disk and clear_stored_data to remove items.

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 (with signal IDs)

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

"Load the signal from measurement.wav" → returns signal_id: sig_a1b2 or: "Generate a test signal with a broken-rotor-bar fault" → sig_c3d4
Calculate motor parameters:

"Calculate motor parameters for a 4-pole motor, 50 Hz supply, running at 1470 RPM"
Compute expected fault frequencies:

"What are the expected fault frequencies for this motor?"
Preprocess the signal:

"Preprocess signal sig_a1b2" → returns new signal_id: sig_e5f6
Analyse the spectrum:

"Compute the FFT spectrum of sig_e5f6" → returns spectrum_id: spec_g7h8
Detect specific faults:

"Check for broken rotor bars in spec_g7h8"
Envelope analysis (optional):

"Compute the envelope spectrum of sig_e5f6"

Quick Diagnosis from Stored Signal

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

Input: signal_id + 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:

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

Supported File Formats

Format	Extensions	Sampling Rate
CSV / TSV	`.csv`, `.tsv`, `.txt`	From time column or user-supplied
WAV	`.wav`	Embedded in header
NumPy	`.npy`	User-supplied

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)

Level	Range
Healthy	≤ −50 dB
Incipient	−50 to −45 dB
Moderate	−45 to −40 dB
Severe	> −35 dB

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.

Citation

If you use this software in your research, please cite it:

@software{dimaggio_mcsa_2025,
  author       = {Di Maggio, Luigi Gianpio},
  title        = {mcp-server-mcsa: MCP Server for Motor Current Signature Analysis},
  year         = 2025,
  url          = {https://github.com/LGDiMaggio/mcp-motor-current-signature-analysis},
  license      = {MIT}
}

GitHub shows a "Cite this repository" button automatically from the CITATION.cff file.

License

MIT — see LICENSE for details.

Add this server

Host a copy with our keys and logs.

Server facts

Tools

Questions

How do I install Motor Current Signature Analysis?

Does Motor Current Signature Analysis need an API key?

What transport does Motor Current Signature Analysis use?

When was Motor Current Signature Analysis last checked?

Documentation

mcp-server-mcsa

Features

Tools (21)

Resources

Prompts

Installation & Setup

Step 1 — Install uv (one-time, if you don't have it)

Step 2 — Verify it works

Step 3 — Add to your MCP client

Claude Desktop

VS Code (Copilot / Continue)

Cursor

Step 4 — Test

Troubleshooting

Data Store

Usage Examples

Real Signal — One-Shot Diagnosis

Step-by-Step Workflow (with signal IDs)

Quick Diagnosis from Stored Signal

Bearing Analysis

Supported File Formats

Fault Detection Theory

Broken Rotor Bars (BRB)

Eccentricity

Stator Inter-Turn Faults

Bearing Defects

Severity Thresholds (dB below fundamental)

Development

Setup

Run tests

Run with MCP Inspector

Lint and type check

Dependencies

Documentation

Citation

License