Machine learning-based failure prediction in industrial maintenance: improving performance by sliding window selection
International Journal of Quality & Reliability Management
ISSN: 0265-671X
Article publication date: 2 November 2022
Issue publication date: 23 May 2023
Abstract
Purpose
Machine learning (ML) models are increasingly being used in industrial maintenance to predict system failures. However, less is known about how the time windows for reading data and making predictions affect performance. Therefore, the purpose of this research is to assess the impact of different sliding windows on prediction performance.
Design/methodology/approach
The authors conducted a factorial experiment using high dimensional machine data covering two years of operation, taken from a real industrial case for the production of high-precision milled and turned parts. The impacts of different reading and prediction windows were tested for three ML algorithms (random forest, support vector machines and logistic regression) and four metrics (accuracy, precision, recall and F-score).
Findings
The results reveal (1) the critical role of the prediction window contingent upon the application domain, (2) a non-monotonic relationship between the reading window and performance, and (3) how sliding window selection can systematically be used to improve different facets of performance.
Originality/value
The study's findings advance the knowledge of ML-based failure prediction, by highlighting how systematic variation of two important but yet understudied factors contributes to the development of more useful prediction models.
Keywords
Acknowledgements
This work was supported by the Federal Ministry for Economic Affairs and Energy [grant: 01MT19005D], Germany. The authors thank Dominique Schubert for providing the data set.
Citation
Leukel, J., González, J. and Riekert, M. (2023), "Machine learning-based failure prediction in industrial maintenance: improving performance by sliding window selection", International Journal of Quality & Reliability Management, Vol. 40 No. 6, pp. 1449-1462. https://doi.org/10.1108/IJQRM-12-2021-0439
Publisher
:Emerald Publishing Limited
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