Mapping industrial time-series to natural language: Making sensor data readable and searchable in plain English
Empowering non-experts to unlock time-series insights
Time series data are sequences of sensor measurements recorded over time (e.g., temperatures, pressures, vibrations) whose evolving shapes encode machine states and anomalies. Understanding these signals lets teams retrieve similar situations, explain anomalies, and reuse know how. Time-series data from industrial sensors saturates today’s production lines —temperatures, pressures, vibrations, and energy readings stream in around the clock. Yet interpreting these signals remains difficult, especially for non-expert engineers and operators. Traditional methods for searching or describing sensor data still depend on domain specialists, manual sketching, or brittle rule-based systems. Such approaches do not scale and struggle to accommodate diverse domains.
