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Economic Perspective on Algorithm Selection for Predictive Maintenance

Title data

Fabri, Lukas ; Häckel, Björn ; Oberländer, Anna Maria ; Töppel, Jannick ; Zanker, Patrick:
Economic Perspective on Algorithm Selection for Predictive Maintenance.
2019
Event: 27th European Conference on Information Systems (ECIS) , 08.-14.06.2019 , Stockholm and Uppsala, Sweden.
(Conference item: Conference , Paper )

Official URL: Volltext

Project information

Project title:
Project's official titleProject's id
Projektgruppe WI Künstliche IntelligenzNo information

Abstract in another language

The increasing availability of data and computing capacity drives optimization potential. In the indus-trial context, predictive maintenance is particularly promising and various algorithms are available for implementation. For the evaluation and selection of predictive maintenance algorithms, hitherto, statis-tical measures such as absolute and relative prediction errors are considered. However, algorithm se-lection from a purely statistical perspective may not necessarily lead to the optimal economic outcome as the two types of prediction errors (i.e., alpha error ignoring system failures versus beta error falsely indicating system failures) are negatively correlated, thus, cannot be jointly optimized and are associ-ated with different costs. Therefore, we compare the prediction performance of three types of algorithms from an economic perspective, namely Artificial Neural Networks, Support Vector Machines, and Ho-telling T² Control Charts. We show that the translation of statistical measures into a single cost-based objective function allows optimizing the individual algorithm parametrization as well as the un-ambig-uous comparison among algorithms. In a real-life scenario of an industrial full-service provider we derive cost advantages of 15% compared to an algorithm selection based on purely statistical measures. This work contributes to the theoretical and practical knowledge on predictive maintenance algorithm selection and supports predictive maintenance investment decisions.

Further data

Item Type: Conference item (Paper)
Refereed: Yes
Keywords: Predictive Maintenance; Algorithm Selection; Economic PerspectiveM; Prediction Error
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Business Administration
Research Institutions
Research Institutions > Affiliated Institutes
Research Institutions > Affiliated Institutes > Fraunhofer Project Group Business and Information Systems Engineering
Research Institutions > Affiliated Institutes > FIM Research Center Finance & Information Management
Faculties
Faculties > Faculty of Law, Business and Economics
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
Date Deposited: 09 Jul 2019 09:06
Last Modified: 27 Sep 2019 12:34
URI: https://eref.uni-bayreuth.de/id/eprint/49886