Optimize business outcomes with reliable predictions about asset failures
Predictive maintenance software promises to help industries determine when to repair or replace their assets. But most software solutions base their predictions on statistical averages. These predictions can be unreliable.
This paper shows how machine learning models can overcome the limitations of statistical averages. It describes how these models can analyze each unit’s behavior, condition and past failures to determine how and when it will fail in the future. The results are reliable predictions that enable industries to optimize business outcomes.