Brings industrial predictive maintenance capabilities into Claude through vibration analysis and bearing fault diagnostics. You get tools for analyzing sensor data against ISO 20816 standards, detecting common bearing defects like misalignment and imbalance, and running ML-based anomaly detection on equipment behavior. Reach for this when you need to interpret vibration readings, diagnose mechanical faults, or build monitoring workflows for rotating machinery. The implementation focuses on frequency domain analysis and pattern recognition typical of condition monitoring systems, letting you work with time series data from accelerometers and other industrial sensors without switching to specialized maintenance software.
claude mcp add --transport stdio io.github.lgdimaggio-predictive-maintenance-mcp uvx predictive-maintenance-mcp