An intelligent maintenance solution that detects early signs of equipment abnormalities and predicts failure risk,
reducing unplanned downtime and improving equipment operating efficiency
Equipment on the manufacturing floor is a core asset that determines productivity and quality, and unexpected equipment failures can lead to production stoppages, delivery delays, and higher maintenance costs.
Conventional breakdown maintenance and schedule-based preventive maintenance struggle to reflect the actual condition of equipment, which can result in unnecessary maintenance or missed signs of failure.
AI-based predictive maintenance collects vibration, temperature, current, pressure, and other data from equipment in real time and detects early signs of abnormalities through AI analysis, supporting an efficient, condition-based maintenance system.
Collects operating data such as vibration, temperature, and current from IoT sensors, PLCs, and equipment control systems
Preprocesses collected data and uses AI models to analyze equipment operating patterns and signs of abnormalities
Assesses the risk level of each machine based on anomaly detection results and alerts on equipment that needs inspection
Reviews equipment condition and maintenance history, performs inspection and maintenance, and manages the results
We monitor each machine's operating status and key sensor data in real time and support maintenance decisions through AI-based anomaly analysis.
We integrate data collected from industrial IoT sensors, PLCs, and equipment control systems, and monitor equipment operating conditions with AI-based anomaly detection and condition analysis. By visualizing each machine's anomalies and risk levels and linking them with maintenance history, we support data-driven maintenance decisions and optimized equipment operation.
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