AN IOT DATA ANOMALY RESPONSE MODEL FOR SMART FACTORY PERFORMANCE MEASUREMENT

The recent rapid proliferation of Internet of Things increases the possibility of IoT failure due to complexity of the network and large data volumes. The demand for clear acquisition of data as an important source of performance measurement presents manufacturing companies with a major challenge. To cope with IoT fault problem, we developed the performance measurement and IoT anomaly response model. Development comprises three steps: (1) configuration of the IoT-based performance measurement process using an Overall Equipment Effectiveness Key Performance Indictor and IoT anomaly response model architecture; (2) implementation of an IoT fault case classification and data anomaly detection and mitigation algorithm, using K-means and statistical inference methods; and (3) validation of the proposed algorithm through experimental simulation. The experimental simulation results show that the proposed algorithm has a positive impact on IoT data anomaly detection and mitigation, thus enabling a response to the IoT fault problem