MCP server for Motor Current Signature Analysis — fault detection in electric motors
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io.github.LGDiMaggio/mcp-server-mcsa is an MCP server that MCP server for Motor Current Signature Analysis — fault detection in electric motors. Its tool list has not been published yet over stdio, sse and http, requires no API key, and scores 79/100 on MCPpedia's security, maintenance and efficiency rubric.
Config is the same across clients - only the file and path differ.
{
"mcpServers": {
"mcsa": {
"args": [
"mcp-server-mcsa"
],
"command": "uvx"
}
}
}Are you the author?
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MCP server for Motor Current Signature Analysis — fault detection in electric motors
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uvx 'mcp-server-mcsa' 2>&1 | head -1 && echo "✓ Server started successfully"
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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_ |