Computational Intelligence-Based Fault Detection in Refrigeration Systems: A Study on Enhancing System Reliability

The utilization of computational intelligence, particularly Artificial Neural Networks (ANNs), for fault detection is of paramount importance as it empowers industries to proactively identify anomalies, leading to improved system reliability, reduced downtime, and enhanced safety. By leveraging the pattern recognition capabilities of ANNs, complex data patterns indicative of faults can be accurately identified and analyzed in real-time, enabling early intervention and preventing potential catastrophic failures. Additionally, the importance of fault detection in refrigeration systems lies in its ability to proactively identify and address potential issues, ensuring optimal performance, energy efficiency, and longevity of the system while preventing costly breakdowns and ensuring product safety and quality. The main aim of this study is to create a computational intelligence model that can accurately depict the energy and exergy performance of a GAX hybrid refrigeration system. Moreover, the model aims to identify potential instrument failures occurring at different parts of the system. The primary findings indicate that creating a numerical database using the governing equations of the GAX system enables the identification of anomalies in the instrumental measurements of operating parameters. Subsequent research aims to incorporate experimental data from a broader range of parameters, encompassing additional sections of the GAX system.