Data-Driven Detection and Prediction of Refrigeration Equipment Failures Using Rough Sets Theory and the Internet of Things

This article presents a system for the detection and prediction of faults in refrigeration equipment developed using rough set theory, a method from artificial intelligence and leveraging Internet of Things (IoT) technology for continuous data collection. The system targets the most frequent failures (fan, compressor, and controller faults), allowing early detection and timely intervention. Measurement data are transmitted to a cloud platform for analysis within a distributed architecture, ensuring scalable and efficient processing. Data-driven diagnostic models were built on rough set theory, enabling decision-making based on incomplete or imprecise data. Experiments conducted on both real and simulated datasets demonstrated high detection effectiveness, with accuracy ranging from 76% to 90% across all monitored fault types. Diagnostic parameters were analyzed to assess the system performance comprehensively. The paper also discusses potential directions for further development, including adaptation to other refrigeration devices and integration of the decision-making system into IoT devices, opening the way for fully predictive maintenance solutions.