Human-in-the-loop control processes in gas turbine maintenance

Abstract

In this applied research paper, we describe an architecture for seamlessly integrating factory workers in industrial cyber-physical production environments. Our human-in-the-loop control process uses novel input techniques and relies on state-of-the-art industry standards. Our architecture allows for real-time processing of semantically annotated data from multiple sources (e.g., machine sensors, user input devices) and real-time analysis of data for anomaly detection and recovery. We use a semantic knowledge base for storing and querying data (http://www.metaphacts.com) and the Business Process Model and Notation (BPMN) for modelling and controlling the process. We exemplify our industrial solution in the use case of the maintenance of a Siemens gas turbine. We report on this case study and show the advantages of our approach for smart factories. An informal evaluation in the gas turbine maintenance use case shows the utility of automated anomaly detection and handling: workers can fill in paper-based incident reports by using a digital pen; the digitised version is stored in metaphacts and linked to semantic knowledge sources such as process models, structure models, business process models, and user models. Subsequently, automatic maintenance and recovery processes that involve human experts are triggered.