Enhancing Fault Detection and Diagnosis in Organic Rankine Cycle systems through Proper Feature Selection in Machine Learning

In the current energy scenario, enhancing energy efficiency is crucial for sustainable development. Organic Rankine Cycle (ORC) systems play a significant role by recovering waste heat and converting it into useful energy. However, the efficiency and robustness of ORC systems are often compromised by various types of faults, leading to increased maintenance costs. This study focuses on fault detection and diagnosis (FDD) to improve system reliability and reduce maintenance expenses in ORC systems. The proposed methodology utilizes machine learning, specifically support vector machines (SVM), to detect and classify faults such as evaporator fouling and operational deficiencies in the expander and pump. A critical aspect of this approach is the careful choice of features for classification, which can either significantly enhance SVM's effectiveness or lead to misclassification of certain faults. Simulation results explore various feature sets to illustrate their impact on the FDD methodology's efficacy. These scenarios are evaluated in terms of prediction accuracy, demonstrating that proper feature selection improves the long-term efficiency and reliability of ORC systems.