Predicting the behavior of solution alternatives within product improvement processes

DS 75-9: Proceedings of the 19th International Conference on Engineering Design (ICED13), Design for Harmonies, Vol.9: Design Methods and Tools, Seoul, Korea, 19-22.08.2013

Year: 2013
Editor: Udo Lindemann, Srinivasan V, Yong Se Kim, Sang Won Lee, John Clarkson, Gaetano Cascini
Author: Abramovici, Michael; Lindner, Andreas; Dienst, Susanne; Fathi, Madjid
Series: ICED
Institution: 1: Ruhr University Bochum, Germany; 2: University of Siegen, Germany
Page(s): 397-406
ISBN: 978-1-904670-52-0
ISSN: 2220-4334


Nowadays an increasing number of industrial products are equipped with sensors, allowing a complete monitoring of the product and its working conditions during the use phase. The data generated by such sensors is mainly used for maintenance purposes. The evaluation of that data can offer valuable input for the improvement of existing product generations. The presented approach offers a methodology to identify improvement potentials and to support decisions within product improvement processes. This approach is based on prescriptive decision theory and uses feedback data in addition to product-specific characteristics and properties. A prediction of future product solution alternatives behavior is realized on the basis of object-oriented Bayesian Networks. The validation of the proposed solution has been demonstrated on the basis of decision processes for the improvement of centrifugal pumps.

Keywords: Product improvement, decision making, prediction, knowledge management


Please sign in to your account

This site uses cookies and other tracking technologies to assist with navigation and your ability to provide feedback, analyse your use of our products and services, assist with our promotional and marketing efforts, and provide content from third parties. Privacy Policy.