Improved Anomaly Detection in Air Conditioners Using IoT Technologies

The prediction of anomalies and the diagnosis of various machines using Internet of Things (IoT) technology is a topic of extensive research. In this study, IoT technology was utilized to develop a system for collecting data and diagnosing existing air conditioners, with a focus on anomaly detection. Building on our previous research, which explored strategies for deploying vibration sensors in various locations to optimize seasonal diagnosis models, this study introduces a method for improving anomaly detection accuracy. The method leverages air-conditioner-specific sensor data analyzed using the MahalanobisTaguchi (MT) method, which calculates the Mahalanobis distance for normal sensor values and vibration data. Discrepancies in sampling rates between vibration and air-conditioner-specific sensor data were addressed using the proposed analysis method.