Timely prediction of equipment faults and failures helps decrease costs for maintenance and repairs, as well as avoid total failure and unwanted repair and replacement costs. Subsequent financial losses can be not only direct, but also indirect - loss of customer confidence and deterioration of the image can cause a long-term decline in profits and outflow of customers. Using predictive analytics to predict breakdowns avoids such problems.
The predictive model answers two questions: what will break and when will break. Equipment failure prediction is carried out both on the basis of accumulated data and data received in real time.