2024/07/26 by Yasir Saleem Afridi, Afridi, Yasir Saleem, Mian Ibad Ali Shah +6
Engineering · #Advanced Measurement and Detection Methods #Artificial Intelligence (cs.AI) #Artificial intelligence #Artificial neural network #Bearing (navigation) #Computer science #Electrical engineering #Engineering #Engineering Diagnostics and Reliability #FOS: Computer and information sciences #FOS: Electrical engineering #Fault (geology) #Geology #Hydropower #Long short term memory #Machine Fault Diagnosis Techniques #Mechanical engineering #Recurrent neural network #Reliability engineering #Systems and Control (eess.SY) #Term (time) #Turbine #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2407.19040
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/07/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.