A Comprehensive Review on Real-Time Performance Assessment & Operational Fault Monitoring in Hydromachinery

Abstract

Hydromachinery is vital for clean and sustainable power generation, where reliable and efficient operation directly supports the stability of hydropower plants. To achieve this, real-time performance tracking and fault monitoring are becoming increasingly important. This review summarizes recent techniques and technologies used for monitoring turbines and their components in operation. Key areas include sensor-based data collection, modern signal processing tools, and artificial intelligence methods for detecting issues such as cavitation, vibration irregularities, pressure fluctuations, and mechanical wear. Methods like wavelet analysis, principal component analysis (PCA), support vector machines (SVM), and digital twins are discussed for their roles in fault diagnosis and performance evaluation. Advances in IoT-enabled monitoring and predictive maintenance are also highlighted, demonstrating their potential to enhance reliability and minimize downtime. The paper further outlines challenges such as harsh operating conditions, large data handling, and the need for accurate predictive models. Future directions are suggested, focusing on hybrid machine learning approaches, adaptive monitoring strategies, and digital twins for smart, autonomous health management of hydro machinery.