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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| Format: | Article |
| Language: | English |
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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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| _version_ | 1846142253042499584 |
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| author | Selver Softic Bahrudin Hrnjica |
| author_facet | Selver Softic Bahrudin Hrnjica |
| author_sort | Selver Softic |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-21235bf1ccbb4525a72b401a225ce6e9 |
| institution | Kabale University |
| issn | 2217-2661 2683-345X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | University of Novi Sad, Faculty of Technical Sciences |
| record_format | Article |
| series | International Journal of Industrial Engineering and Management |
| spelling | doaj-art-21235bf1ccbb4525a72b401a225ce6e92024-12-03T12:51:36ZengUniversity of Novi Sad, Faculty of Technical SciencesInternational Journal of Industrial Engineering and Management2217-26612683-345X2024-12-01154320337http://doi.org/10.24867/IJIEM-2024-4-366366Case Studies of Survival Analysis for Predictive Maintenance in ManufacturingSelver Softic0Bahrudin Hrnjica1IT & Business Informatics, CAMPUS 02 University of Applied Sciences, Graz, AustriaUniversity of Bihac, Faculty of Technical Engineering, Bihac, Bosnia and HerzegovinaThe 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.http://www.ijiemjournal.uns.ac.rs/images/journal/volume15/IJIEM_366.pdfsurvival analysispredictive maintenancemachine learning |
| spellingShingle | Selver Softic Bahrudin Hrnjica Case Studies of Survival Analysis for Predictive Maintenance in Manufacturing International Journal of Industrial Engineering and Management survival analysis predictive maintenance machine learning |
| title | Case Studies of Survival Analysis for Predictive Maintenance in Manufacturing |
| title_full | Case Studies of Survival Analysis for Predictive Maintenance in Manufacturing |
| title_fullStr | Case Studies of Survival Analysis for Predictive Maintenance in Manufacturing |
| title_full_unstemmed | Case Studies of Survival Analysis for Predictive Maintenance in Manufacturing |
| title_short | Case Studies of Survival Analysis for Predictive Maintenance in Manufacturing |
| title_sort | case studies of survival analysis for predictive maintenance in manufacturing |
| topic | survival analysis predictive maintenance machine learning |
| url | http://www.ijiemjournal.uns.ac.rs/images/journal/volume15/IJIEM_366.pdf |
| work_keys_str_mv | AT selversoftic casestudiesofsurvivalanalysisforpredictivemaintenanceinmanufacturing AT bahrudinhrnjica casestudiesofsurvivalanalysisforpredictivemaintenanceinmanufacturing |