Performance Comparison of Machine Learning Fault Detection and Diagnosis Algorithms in Organic Rankine Cycle Systems

This study investigates fault detection and diagnosis (FDD) techniques aimed at enhancing the reliability and
operational efficiency of organic Rankine cycle (ORC) systems used for waste heat recovery. By improving fault
management, this approach can significantly reduce maintenance costs and unplanned downtime, contributing to
the broader adoption of ORC technology in sustainable energy applications. The proposed methodology leverages
advanced machine learning algorithms, specifically support vector machines (SVM) and Random Forest (RF), to
identify and classify critical system faults. These faults include evaporator fouling, the presence of non-condensable
gases, and mechanical issues in the expander and pump components. To evaluate the performance of the FDD
framework, simulation-based experiments are conducted to address practical challenges such as data scarcity and
noise, which are common in industrial applications. Results demonstrate the robustness of the SVMand RF models
in accurately detecting and diagnosing faults, highlighting their potential to maintain high system performance
in real-world scenarios. Additionally, the analysis provides insights into selecting appropriate strategies based on
the quantity and quality of data available. Furthermore, the study explores the trade-offs between computational
efficiency and diagnostic accuracy, offering insights into the applicability of these techniques for online monitoring
systems. The findings underscore the critical role of machine learning in predictive maintenance strategies, paving
the way for smarter and more resilient energy recovery solutions.