Summary of A Fault Prognostic System For the Turbine Guide Bearings Of a Hydropower Plant Using Long-short Term Memory (lstm), by Yasir Saleem Afridi et al.
A Fault Prognostic System for the Turbine Guide Bearings of a Hydropower Plant Using Long-Short Term Memory (LSTM)
by Yasir Saleem Afridi, Mian Ibad Ali Shah, Adnan Khan, Atia Kareem, Laiq Hasan
First submitted to arxiv on: 26 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a machine learning-based approach to predicting turbine bearing faults in Hydropower Plants (HPPs). As HPPs become more complex and efficient, their operation and maintenance (O&M) require more intelligent predictive strategies. The proposed method utilizes Long Short-Term Memory (LSTM) algorithm to develop an artificially intelligent fault prognostics system for turbine bearings. Initially trained and tested with bearing vibration data from a test rig, the model is then validated using realistic data obtained from an HPP operating in Pakistan via Supervisory Control and Data Acquisition (SCADA) system. The LSTM-based model demonstrates effective predictions of bearing vibration values, achieving low Root Mean Squared Error (RMSE). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper aims to develop a smart way to predict problems with turbine bearings at Hydropower Plants. As these plants get more complex and efficient, it’s harder to keep them running smoothly. The team uses special computer algorithms called Long Short-Term Memory (LSTM) to create a system that can foresee potential issues before they happen. They train the model using data from test equipment and real-world data from an actual power plant in Pakistan. The results show that this approach is very good at predicting bearing vibrations, which is important for keeping these plants running efficiently. |
Keywords
» Artificial intelligence » Lstm » Machine learning
