Predictive Maintenance Platform
utilizing ML algorithms and state of the art techniques.
Real-time analysis of the sensors’ signals.
Detection of instant deviations from the normal operation
Detection of constantly evolving deviations
Identify the failure mode of a fault with high accuracy and dependability.
Utilization of techniques that require minimal historical data footprint to identify rare events
Neural Network models to identify frequent faults
Custom made solutions for fault identification of special cases
Timely be alerted of prominent faults in your mechanical
assets and prepare in advance the mitigation plan.
Fault prediction based on timeseries analysis
Fault prediction identifying re-occuring patterns of events
Adjustable prediction time span
Remaining Useful Life Estimation
Be informed constantly about the remaining life and production capabilities of your assets
Long-term health check
Accurate estimation of the remaining product cycles of operation time
Continuous optimization of estimation accuracy
Live visual representation of the collected signals from the sensors and the state of the mechanical assets
Comparative study on historical data
Adaptation of the user interface to the requirements of the maintenance departments
Development of multi-parametric indicators for the accurate representation of the health of the assets
Receive timely notifications and provide your feedback to improve the accuracy of the results
Automatic work order creation to the AIMMS software or other CMMS
Successful faults prediction using Machine Learning
Using powerful AI algorithms up to 72 hours before fault occurance
Minimize downtime in the production
Custom solutions per machine
Custom algorithms for each failure mode