AI driven Snotra Fault Detection & Classification
The artificial intelligence detects anomaly
What can you takeaway as a user using AI driven Snotra FDC?
AI that self-configures and monitors.
Artificial Intelligence FDC is peformed just by attaching to IoT sensor or equipment sensor!
Autonomous FDC without prior statistical analysis and specification setting.
FDC, which simultaneously monitors hundreds of process equipment constituting the manufacturing process, can change over time and change the process conditions of the equipment whenever a new product is added. Therefore, the precision and accuracy of the SPEC setting determines the success or failture of FDC.
AI based false alarm prevention algorithm application
In the instances of USPC, real-time detection is possible with change point detection algorithm and peak signal detection algorithm. In case of MSPC, speed is improved by removing unnecessary variables while checking real-time correlation and CoV, and the accuracy of PLS/PCA is improved. In particular, it has the advantage of being able to create PCA at any time by dynamically setting the parameters.
Real-time population statistical technology application by AI.
Without the need for excessive time in as data collection, processing, and analysis to create a model, AI driven FDC does modelling automatically. In the instances of MSPC, technical advisors applied our own technology to automatically create a model in real time, which could only be created by creating a process window frame by setting parameters in advance.