Participants
Etxetar
Universidad Politécnica de Madrid
Aingura insights
Delivery
Data Acquisition
Preprocessing
Transportation and storage
Transformation
Delivery
Data Acquisition
Preprocessing
Transportation and storage
Transformation
Sectors
Industry
Applications
Maintenance
Condition Monitoring
impact
- Detect deviations in the process parameters below 15% of the cycle time, commonly invisible to the deployment technicians.
- Increased productivity by an average of 6 hours a day.
Developed AI Technologies
- Data intelligence.
- Machine Learning.
- Pattern analysis, prediction and intelligent classification.
Challenges
- To increase in machine availability in high production systems in order to maintain or exceed 95%.
- To search for inefficiencies in the operating pattern that represent losses of time and prevent compliance with line cycle times.
- Reduction of machine deployment times by detecting imperceptible deviations.
results
- Dataset size: 12GB
- Number of variables: 22
- Tiempo de muestreo: 100ms
- Development of an algorithm for the detection of deviations based on a hybrid system between Kernel Density Estimation (KDE) and Hidden Markov Models (HMM) for training and detection of deviations based on machine data acquired at 100 ms.
- Offline data analysis system that has allowed to find deviations below 2 seconds, as shown in graphics bellow.
- The inefficiency detected is related to a CNC code line that unexpectedly moved the drilling references in the B axis. This error would have been practically impossible to detect with the naked eye.
