Utility companies that operate hydro turbines have a vested interest in performing regular maintenance to decrease asset downtime and prevent unexpected failures or catastrophic events. In this case study, learn how one leading hydropower utility company applied machine learning to better analyze problematic turbine behavior and predict uncommon failures. Read how the company:

Identified a large-scale outage with one month advanced warning.

Gained deeper insight into their asset performance, enabling more robust root-cause analysis.

Recognized patterns for quick, proactive remediation of problems before they disrupted operations.