Generation of Complex Data for AI-based Predictive Maintenance Research with a Physical Factory Model

摘要

Manufacturing systems naturally contain plenty of sensors which produce data primarily used by the control software to detect relevant status information of the actuators. In addition, sensors are included in order to monitor the health status of specific components, which enable to detect certain known, frequently occurring faults or undesired states of the system. While the identification of a failure by using the data of a sensor dedicated explicitly to its detection is a rather straightforward machine learning application, the detection of failures which only have an indirect effect on the data produced by a couple of other sensors is much more challenging. Therefore, a combination of different methods from Artificial Intelligence, in particular, machine learning and knowledge-based (semantic) approaches is required to identify relevant patterns (or failure modes). However, there are currently no appropriate research environments and data sets available that can be used for this kind of research. In this paper, we propose an approach for the generation of predictive maintenance data by using a physical Fischertechnik model factory equipped with several sensors. Different ways of reproducing real failures using this model are presented as well as a general procedure for data generation.