mcp-server-mcsa 0.1.2

MCP server for Motor Current Signature Analysis (MCSA) — spectral analysis, fault frequency calculation, and fault detection in electric motors

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.

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)

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
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
compute_spectrum	Single-sided FFT amplitude spectrum
compute_power_spectral_density	Welch PSD estimation
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
run_full_diagnosis	Complete MCSA diagnostic pipeline from signal array
diagnose_from_file	Complete MCSA diagnostic pipeline directly from file

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

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:

Using uvx

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

Using pip

{
  "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

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.

License

MIT — see LICENSE for details.