To benefit the customer
AI for Enterprise
Sound-based Predictive mainte -nance for production equipment
calculating production performance and detecting various situations
such as unexpected system down and checking product quality through
AI deep learning analysis using sound data.
In the past, Specialized personnel or embedded applications such
as complex machines were required for monitoring machines. Now,
With various AI voice recognition technologies, It’s possible to detect
unusual signals in sound data made by factories and machines.
Monitoring equipment operation status, Determining its lifespan, Checking defective products through deep learning analysis using sound sensors.
· Sound Analysis - Analysis of phenomena, predictions, and causes → Detection of equipment abnormalities
· Regression Analysis - phenomenon, optimization, cause analysis → facility lifecycle management and optimization operation
▲ Algorithm conceptual diagram – After featuring sound data with SFTP, the filtered data will be processed by CNN for deep
learning. The classified result will be used for determining valid production performance and equipment status.
01 Real-time status monitoring for production (operating time, maximum/actual
production quantity), and replacement related information.
02 Provision of facility management, mold management, mold parts management,
mold removal/installation history, number of strokes by mold, production status
of machine equipment
Early detection of
of robot/facility data
Prevent accidents by
increasing system safety
01 Notification for maintenance schedule according to facility operation rate.
02 Automatic facility status detection and notification.
03 Flexible, Fast, and scalable data processing
04 Automatic data-based crash prediction.
05 Saving cost for pre-detection