  Hydroelectricity, being a renewable source of energy, globally fulfills the
electricity demand. Hence, Hydropower Plants (HPPs) have always been in the
limelight of research. The fast-paced technological advancement is enabling us
to develop state-of-the-art power generation machines. This has not only
resulted in improved turbine efficiency but has also increased the complexity
of these systems. In lieu thereof, efficient Operation & Maintenance (O&M) of
such intricate power generation systems has become a more challenging task.
Therefore, there has been a shift from conventional reactive approaches to more
intelligent predictive approaches in maintaining the HPPs. The research is
therefore targeted to develop an artificially intelligent fault prognostics
system for the turbine bearings of an HPP. The proposed method utilizes the
Long Short-Term Memory (LSTM) algorithm in developing the model. Initially, the
model is trained and tested with bearing vibration data from a test rig.
Subsequently, it is further trained and tested with realistic bearing vibration
data obtained from an HPP operating in Pakistan via the Supervisory Control and
Data Acquisition (SCADA) system. The model demonstrates highly effective
predictions of bearing vibration values, achieving a remarkably low RMSE.
