Sensor technology has become increasingly important (e.g., Industry 4.0 and IoT). Large numbers of machines and products are equipped with sensors to constantly monitor their condition. Usually, the condition of an entire system is inferred through sensors in parts of the system by means of a multiplicity of methods and techniques. This so-called condition monitoring can thus reduce the downtime costs of a machine through improved maintenance scheduling. However, for small components as well as relatively inexpensive or immutable parts of a machine, sometimes it is not possible or uneconomical to embed sensors.
This chapter introduces a system-oriented concept of how to monitor individual components of a complex technical system without including additional sensor technology. By using already existing sensors from the environment combined with machine learning techniques, we are able to infer the condition of a system component, without actually observing it. As a consequence, condition monitoring or additional services based on the component’s behavior can be developed without overcoming the challenges of sensor implementation. In order to show the feasibility of the presented concept, we also implement an industrial use case.