Makine öğrenimi algoritmalarını kullanarak buzdolaplarında otomatik arıza tespiti

The sustainable functioning of refrigerators is crucial in residential and commercial settings. These appliances are used continuously throughout the day, and any failure can lead to food spoilage, negatively impacting brand reputation. Therefore, having an efficient failure detection system that can identify and diagnose any problems instantly is essential. This paper proposes a novel machine learning pipeline that uses online sensor data from the refrigerators of anonymous customers and a feedback mechanism to inform customer service about the detected failure remotely. The performance of the system is evaluated through a real-life pilot project, and the results indicate that the proposed method achieves high accuracy in detecting various types of failure. Applying the proposed approach prevents food spoilage, reduces maintenance costs while increasing customer satisfaction, and enhances the reliability and safety of refrigerators.