Motor Current Signature Analysis
by lgdimaggiocommunityv0.2.1
4
This MCP server provides non-invasive spectral analysis and fault detection in electric motors using Motor Current Signature Analysis (MCSA) techniques.
From the registry: MCP server for Motor Current Signature Analysis — fault detection in electric motors
Install & configure
Prerequisites
- requiredInstall uvInstall uv Python package manager (provides uvx).
https://docs.astral.sh/uv/$ curl -LsSf https://astral.sh/uv/install.sh | sh
Install in your client
Paste into the AI you're chatting with — it will figure out the rest
Please install the `mcp-server-mcsa` MCP server into my current AI client (that's you).
Required prerequisites (do these first if not already done):
- **Install uv** — Install uv Python package manager (provides uvx). Run: `curl -LsSf https://astral.sh/uv/install.sh | sh` (https://docs.astral.sh/uv/)
Canonical MCP server config (stdio transport):
- command: `uvx`
- args: ["mcp-server-mcsa"]
- optional environment variables:
- `MCSA_DATA_DIR`: Override path to persistent data store (default: ~/.mcsa_data/). (example: `~/.mcsa_data`)
Add this MCP server to my current client's config in the correct format for you. If you need secrets or credentials I haven't provided, ASK me — do not invent values or leave raw placeholders. After adding it, tell me how to verify the server is connected.Tools (21)
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
Environment variables
MCSA_DATA_DIRConfigurable directory for storing signals and spectraPrerequisites
- • Python 3.10+
Packages
mcp-server-mcsa
pypiv0.2.1transport: stdio
Details
- Language
- Python
- License
- MIT
- Released
- Feb 9, 2026
- Last commit
- Feb 16, 2026
- Registry name
- io.github.LGDiMaggio/mcp-server-mcsa