Tells the future

Go beyond monitoring: Automate analysis to take actions

  • The PRECOG product can predict the failure of an electric motor two weeks in advance
  • Works with DC, single-phase AC, and three-phase AC electric motors that can be part of complex machines
  • Logistics and maintenance costs can be reduced
  • It can be used for water and oil pumps, production lines, robotic arms, railway turnouts, and essentially any complex equipment that contains an electric motor or electronics where the high availability is crucial.
  • The forecasting accuracy of PRECOG is over 90%, and it continuously improves as the AI learns and adapts to specific devices
  • PRECOG utilizes Artificial Intelligence, employing 14 different probabilistic algorithms
  • PRECOG includes prediction algorithms and the trained AI solution
  • We also undertake the complete execution of projects, including the integration of third-party devices and monitoring applications
  • Simple data structure
  • Suitable for motors in continuous operation as well as those performing periodic motion, where monitoring the entire waveform

Example of how PRECOG works

In this example, you can see how Precog raises an alarm 14 days before a fault occurs in an electric motor.

You can find more examples on the Try Precog page.

Accuracy and Evaluation

Excellent results backed by over 14 years of experience and 7 billion measurement data points!

The algorithms have been tested based on > 1,500 different assets and indicators, using over 10 years of diverse technical measurement data.

The accuracy of the PRECOG algorithms is measured using a custom evaluation and testing program designed for this purpose.

Various technical parameters are continuously evaluated based on criteria including the ones listed below, and we continuously improve the efficiency of the predictions by appropriately adjusting these parameters.

Ways to assess the accuracy of the capabilities of PRECOG prediction algorithms

  • Fault prediction ratio (above 90%):

    • We count how many faults the algorithm successfully predicted within a given period, including both the faults that were mitigated in time and those that occurred.
    • Formula: Number of correctly predicted faults / sum of faults happened

  • Prediction in time (above 90%):

    • PRECOG is reliable in predicting medium and long-term faults, as well as sudden short-term faults.
    • Formula: Number of predictions made in time / total number of predictions made
      Click here to see examples

  • Critical alerts ratio (~100%):

    • Almost all detected faults trigger at least one red alert from PRECOG before they occur, timely warning the operator.
    • Formula: Number of red alerts / total number of alerts

  • False critical alerts ratio (less than 1%):

    • PRECOG algorithms are designed to minimize false alerts, thereby preventing unnecessary distractions for the operator.
    • Formula: Number of false red alerts / total number of alertsalse critical alerts ratio (less than 1%)

  • Consistency of predictions (above 80%):

    • Continuous alerts about a potential fault draw attention to it, and this indicator shows the effectiveness of the prediction to the monitoring personnel.
    • Formula: Number of alerts in the relevant period for a given fault / length of the given period

Case Studies

Turnout prediction projects in the railway sector

View an example of turnout prediction for railway switches performing periodic motion.
The turnouts use single-phase or three-phase AC electric motors.
Learn More About Complex Periodic Motion Machines
Railway industry

Prediction project in the telecommunication industry

More than 1500 different IT KPIs and Telecommunication Network KPIs, backed by over a decade of measured data
Learn More About Prediction Of Different Telecommunication KPIs
Telecommunication industry

Smart maintenance maturity assessment diagram

Measuring various physical aspects of IoT devices

Collect and store telemetry data

Monitoring, administration
and general AI

  • Basic fault detection

  • Helps to find the right physical aspects

  • Provide approximate reference data

  • Due to the general model, the success rate is significantly lower

Precog statistical algorithms

  • Designed for industrial use

  • Typically for electric motors and machines performing periodic motion

  • Combines multiple different probability calculation algorithms

  • High success rate, as it is specifically tailored

PRECOG AI

  • Automatically learns the behavior of the device

  • Development of algorithms tailored to the specific device

  • Continuously improves the efficiency of the algorithms

  • Automatic reference data determination

  • Short learning time

  • Very accurate forecasts, analysis and advice