Transferable and self-learning Online Monitoring System for electrical household appliances

Abstract In this paper an Online Monitoring System is presented, which enables a universal condition monitoring of electrical household appliances. The approach involves recording the power consumption of the respective device and evaluating the measured data using machine learning methods and tools. Thereby an artificial intelligence learns the normal condition of the examined device and can subsequently perform a monitoring. A k-Means algorithm is used for clustering and a One-class Support Vector Machine for classification. This makes it possible to detect anomalies in the operating modes and behavior of the device during subsequent operation. In addition to the methodology, the design and real application of a prototype is presented, in which a suitable sensor technology and a software with artificial intelligence are implemented.