Case Studies of Survival Analysis for Predictive Maintenance in Manufacturing
The Predictive Maintenance (PdM) as a tool for detecting future failures in manufacturing was recognized as an innovative and effective method. Different approaches for PdM have been developed to compromise the availability of data and the demanding needs for probability estimation. The Survival Ana...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
University of Novi Sad, Faculty of Technical Sciences
2024-12-01
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| Series: | International Journal of Industrial Engineering and Management |
| Subjects: | |
| Online Access: | http://www.ijiemjournal.uns.ac.rs/images/journal/volume15/IJIEM_366.pdf |
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| Summary: | The Predictive Maintenance (PdM) as a tool for detecting future failures in manufacturing
was recognized as an innovative and effective method. Different approaches for PdM have
been developed to compromise the availability of data and the demanding needs for probability
estimation. The Survival Analysis (SA) method was used in this paper for the probability
estimation of machine failure. The paper presents the use of the two most popular SA
models: Kaplan-Meier non-parametric and Cox proportional hazard models on two different
datasets to present the methodology and the possibilities for applications in manufacturing.
By using the first SA model, the results show the probability of a machine or component part
to survive a certain amount of time. The Cox proportional model was used to find out the
most significant covariates in the observed dataset which have an influence on survival time.
The analysis showed that the use of SA in the PdM is a challenging task and can be used as
an additional tool for failure analysis and maintenance planning. |
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| ISSN: | 2217-2661 2683-345X |