Fault Diagnosis and Condition Monitoring in Hydropower Systems

Hydropower installations represent a vital component of sustainable energy infrastructure, yet their complex mechanical and hydraulic interactions pose significant maintenance challenges. Fault diagnosis and condition monitoring comprise a suite of approaches aimed at detecting, localising and predicting component degradation before it evolves into serious failure. Modern systems integrate sensors, data acquisition networks and advanced analytics to track performance indicators such as vibration, pressure, flow rate and electrical output. Information on normal operating states is compared against real‐time observations to identify anomalies, while trends in degradation indices inform predictive maintenance schedules. By enabling targeted interventions, these methods reduce unplanned downtime, extend equipment life and optimise overall plant efficiency. Recent advances leverage machine learning, signal processing and statistical modelling to accommodate variable load conditions, heterogeneous data quality and evolving fault modes, yielding more robust and adaptive monitoring strategies with global relevance for hydropower operators.

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Fault Diagnosis and Condition Monitoring in Hydropower Systems publication trend

The graph below shows the total number of articles in fault diagnosis and condition monitoring in hydropower systems across all publications each year (not limited to Nature Index journals).

Technical terms

Fault diagnosis: The process of detecting, locating and identifying the nature of faults in system components.

Condition monitoring: Continuous or periodic tracking of equipment health through sensor measurements and data analysis.

Predictive maintenance: Use of data‐driven models to forecast impending failures and schedule interventions before breakdowns occur.

Anomaly detection: Identification of observations or patterns in data that deviate significantly from established normal behaviour.

Vibration analysis: Examination of mechanical oscillations to infer component condition and reveal signs of wear or imbalance.

References

1. Analysis of hydropower plant guide bearing vibrations by machine learning based identification of steady operations. Renewable Energy (2024).
2. A Vibration Fault Identification Framework for Shafting Systems of Hydropower Units: Nonlinear Modeling, Signal Processing, and Holographic Identification. Sensors (2022).
3. An Ensemble Prognostic Method of Francis Turbine Units Using Low-Quality Data under Variable Operating Conditions. Sensors (2022).