New technologies are driving the advancing digitalization of both consumer and industrial environments. Especially in maintenance, new possibilities to predict malfunctions and failures arise by real-time analytics of relevant data streams. In the context of this paper, the development and implementation of a predictive maintenance system based on low-cost embedded systems and sensor technology from the consumer sector will be presented. This includes the implementation of the device, its connection to the Internet of Things (IoT) as well as the application of machine learning. The system developed will be demonstrated at a robotic cell at the Institute of Production Systems (IPS).