Enables exploration of manufacturing equipment data and analysis of failure risk, supporting monitoring, failure classification, and anomaly detection through natural language.
Enables AI assistants to analyze plant-floor data using OEE, Pareto, SPC, and yield-loss calculations, providing continuous-improvement insights from manufacturing data.
Enables predictive maintenance for electric motors by analyzing stator current signals to detect faults like broken rotor bars, bearing defects, and eccentricity, using spectral and envelope analysis techniques.
Enables natural language analysis of mechanical test data files (CSV, TDMS, MDF) by providing tools for channel statistics, spectrum analysis, rainflow fatigue counting, thermal state detection, and report generation.
Enables engineers and plant managers to interact with manufacturing systems using natural language, providing machine health analysis, KPI dashboards, predictive maintenance, and automated workflow execution via MCP.
Enables AI assistants to perform data analysis on local datasets through natural language, orchestrating Python, R, SQL, Spark, Jupyter, Power BI, and Tableau engines for statistics, machine learning, dashboard generation, and batch processing.