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MCP server for Motor Current Signature Analysis — fault detection in electric motors
MCP server for Motor Current Signature Analysis — fault detection in electric motors
Valid MCP server (1 strong, 3 medium validity signals). 4 known CVEs in dependencies (0 critical, 3 high severity) Package registry verified. Imported from the Official MCP Registry.
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Add this to your MCP configuration file:
{
"mcpServers": {
"mcp-server": {
"args": [
"mcp-server-mcsa"
],
"command": "uvx"
}
}
}From the project's GitHub README.
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.
.npy files~/.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| 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 |
diagnose_from_file | Complete MCSA diagnostic pipeline directly from file |
list_stored_data | List all signals and spectra persisted on disk |
clear_stored_data | Delete one or all stored items from disk |
| URI | Description |
|---|---|
mcsa://fault-signatures | Reference table of fault signatures, frequencies, and empirical thresholds |
| Prompt | Description |
|---|---|
analyze_motor_current | Step-by-step guided workflow for MCSA analysis |
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/uvxcommands are available.
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.
Pick your client and add the configuration below. No other steps are required.
Open the config file:
%APPDATA%\Claude\claude_desktop_config.json~/Library/Application Support/Claude/claude_desktop_config.jsonAdd 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.
Create (or edit) .vscode/mcp.json in your workspace:
{
"servers": {
"mcsa": {
"command": "uvx",
"args": ["mcp-server-mcsa"]
}
}
}
Go to Settings → MCP Servers → Add new server:
commanduvx mcp-server-mcsaIn 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-mcsacommand may not be in your PATH, causing a "server disconnected" error. In that case, find your Python path withpython -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
uvxavoids 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
| 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. |
Signals and spectra are persisted to disk as compressed .npz files
in ~/.mcsa_data/ (configurable via the MCSA_DATA_DIR environment
variable). This means:
sig_xxxx,
spec_xxxx) and compact summaries are returned to the LLM.~/.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.
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.
Load a measured signal (or generate a synthetic one):
"Load the signal from
measurement.wav" → returnssignal_id: sig_a1b2or: "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"
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
For bearing fault analysis, you need the bearing geometry (number of balls, ball diameter, pitch diameter, contact angle). The server will:
| Format | Extensions | Sampling Rate |
|---|---|---|
| CSV / TSV | .csv, .tsv, .txt | From time column or user-supplied |
| WAV | .wav | Embedded in header |
| NumPy | .npy | User-supplied |
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.
Sidebands at $f_s \pm k \cdot f_r$ where $f_r$ is the rotor mechanical frequency.
Sidebands at $f_s \pm 2k \cdot f_r$ due to winding asymmetry.
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).
| 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.
git clone https://github.com/LGDiMaggio/mcp-motor-current-signature-analysis.git
cd mcp-motor-current-signature-analysis
uv sync --dev
uv run pytest
uv run mcp dev src/mcp_server_mcsa/server.py
uv run ruff check src/ tests/
uv run pyright src/
For a detailed reference of every tool, resource, and prompt — including parameter tables, diagnostic workflows, integration patterns, and severity thresholds — see the Usage Guide.
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.cfffile.
MIT — see LICENSE for details.
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