Condition Monitoring Python Packages

bearing-datasets

Python library to download public bearing and rotating-machinery fault datasets (CWRU, Paderborn, XJTU-SY, FEMTO, IMS, …) from their original sources and convert them into one Parquet format, with the same columns and labels for every dataset.

iaes

IAES — Industrial Asset Event Standard. A vendor-neutral event specification for industrial asset measurements, diagnoses, and maintenance intents.

predictive-maintenance-mcp

MCP server for predictive maintenance and machinery fault diagnosis. Gives AI assistants evidence-based vibration analysis - FFT, envelope, bearing fault detection, ISO 20816-3 severity - with a measured, blind CWRU benchmark. Local-first: raw signals never leave your machine. Includes a Claude Code plugin.

mcp-server-mcsa

MCP server for Motor Current Signature Analysis (MCSA) — spectral analysis and fault detection in electric motors in natural language through LLMs

cwru-plus

Ultra-fast, customizable CWRU dataset pipeline optimized for Deep Learning, Domain Adaptation, and parallel digital signal processing.

iflow-mcp-lgdimaggio-predictive-maintenance-mcp

MCP server for predictive maintenance and machinery fault diagnosis. Gives AI assistants evidence-based vibration analysis - FFT, envelope, bearing fault detection, ISO 20816-3 severity - with a measured, blind CWRU benchmark. Local-first: raw signals never leave your machine. Includes a Claude Code plugin.

signalmap

Unsupervised anomaly detection and predictive maintenance for machine sensor data — vibration, acoustics, motor current — with signed verdicts anyone can verify offline.

mseep-predictive-maintenance-mcp

An open-source MCP server that turns LLMs into condition monitoring assistants for reliability engineers. It gives your AI assistant evidence-based vibration diagnostics: machine fault detection, severity assessment and diagnostic reports, for faster predictive maintenance decisions. Also available as a Claude Code plugin.

statorscope

Motor Current Signature Analysis that knows when to refuse. Detect broken rotor bars, stator faults and eccentricity from 3-phase current - and get told when your data can't support the answer.

modenexus

Compile system models to d-DNNF circuits, then diagnose, track, and learn on one artifact: exact posteriors, ranked diagnoses, mode tracking, EM — with a PyTorch backend for GPU inference.

mcift

Deterministic multichannel anomaly-detection and condition-monitoring research toolkit.