The current work presents a decision support system architecture for evaluating the features representing the health status to predict maintenance actions and remaning useful life of component. The evaluation is possible through pattern analysis of past and current measurements of the focused research components. Data mining visualization tools help in creating the most suitable patterns and learning insights from them. Estimations like features split values or measurement frequency of the component is achieved through classification methods in data mining. This paper presents how the quantitative results generated from data mining can be used to support decision making of domain experts.
CC BY-NC-ND 4.0
Edited by Lihui Wang
The presented research activities have received funding from the Knowledge Foundation (KKS), Volvo Cars Corporation (VCC), Eurofins and Autokaross i Floby under the research project Efficient Equipment Engineering (E3) in University of Skövde.