Preventive Maintenance powered by Machine Learning

Fayrix Machine Learning solution collects data from equipment sensors, analyses it, predicts faults and forecasts optimal schedule for maintenance

of Fault Prediction

Preventive maintenance generally includes two tasks: fault prediction and optimization model development which predicts optimal schedule for maintenance activities. Optimization model analyzes current equipment load, working schedule, new equipment purchase schedule. The model evaluates risks related to equipment failure and downtime. Faults are forecasted using both historical and real-time data.

Lack of balance between positive and negative cases

Lack of relevant data from equipment sensors (no values or the same values)

Data is of high-dimentionality, but disparse


Preventive maintenance is crucial for high-load systems. For example, in the airline and railway industry late equipment repair and maintenance can not only impose financial costs but also cause lethal accidents.

Data Driven Predictive Maintenance

The more sources of data are used to identify patterns, the higher the accuracy of failure prediction is achieved. Clues can be found in the most unexpected sources.


  • Increase equipment life cycle and improved performance
  • Safe working conditions for a reliable employer brand image
  • Decreased maintenance costs, saving on consumables and spare parts (oil, gas, etc.)
  • Reduced equipment downtime period
  • Prevented lawsuits and reduced legal costs
  • Optimized maintenance schedule

Do you want to introduce predictive maintenance to your business and cut related costs? Contact us now!