Exploring statistical and machine learning methods for modeling probability distribution parameters in downtime length analysis: a paper manufacturing machine case study
Abstract Manufacturing companies focus on improving productivity, reducing costs, and aligning performance metrics with strategic objectives. In industries like paper manufacturing, minimizing equipment downtime is essential for maintaining high throughput. Leveraging the extensive data generated by...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-11-01
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| Series: | Journal of Big Data |
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| Online Access: | https://doi.org/10.1186/s40537-024-01030-4 |
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| author | Vladimir Koković Kosta Pavlović Andjela Mijanović Slavko Kovačević Ivan Mačužić Vladimir Božović |
| author_facet | Vladimir Koković Kosta Pavlović Andjela Mijanović Slavko Kovačević Ivan Mačužić Vladimir Božović |
| author_sort | Vladimir Koković |
| collection | DOAJ |
| description | Abstract Manufacturing companies focus on improving productivity, reducing costs, and aligning performance metrics with strategic objectives. In industries like paper manufacturing, minimizing equipment downtime is essential for maintaining high throughput. Leveraging the extensive data generated by these facilities offers opportunities for gaining competitive advantages through data-driven insights, revealing trends, patterns, and predicting future performance indicators like unplanned downtime length, which is essential in optimizing maintenance and minimizing potential losses. This paper explores statistical and machine learning techniques for modeling downtime length probability distributions and correlation with machine vibration measurements. We proposed a novel framework, employing advanced data-driven techniques like artificial neural networks (ANNs) to estimate parameters of probability distributions governing downtime lengths. Our approach specifically focuses on modeling parameters of these distribution, rather than directly modeling probability density function (PDF) values, as is common in other approaches. Experimental results indicate a significant performance boost, with the proposed method achieving up to 30% superior performance in modeling the distribution of downtime lengths compared to alternative methods. Moreover, this method facilitates unsupervised training, making it suitable for big data repositories of unlabelled data. The framework allows for potential expansion by incorporating additional input variables. In this study, machine vibration velocity measurements are selected for further investigation. The study underscores the potential of advanced data-driven techniques to enables companies to make better-informed decisions regarding their current maintenance practices and to direct improvement programs in industrial settings. |
| format | Article |
| id | doaj-art-f5f35dff3b4d416687806895bad123b6 |
| institution | Kabale University |
| issn | 2196-1115 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj-art-f5f35dff3b4d416687806895bad123b62024-11-10T12:29:35ZengSpringerOpenJournal of Big Data2196-11152024-11-0111112210.1186/s40537-024-01030-4Exploring statistical and machine learning methods for modeling probability distribution parameters in downtime length analysis: a paper manufacturing machine case studyVladimir Koković0Kosta Pavlović1Andjela Mijanović2Slavko Kovačević3Ivan Mačužić4Vladimir Božović5Faculty of Engineering, University of KragujevacFaculty of Natural Sciences and Mathematics, University of MontenegroFaculty of Natural Sciences and Mathematics, University of MontenegroFaculty of Electrical Engineering, University of MontenegroFaculty of Engineering, University of KragujevacFaculty of Natural Sciences and Mathematics, University of MontenegroAbstract Manufacturing companies focus on improving productivity, reducing costs, and aligning performance metrics with strategic objectives. In industries like paper manufacturing, minimizing equipment downtime is essential for maintaining high throughput. Leveraging the extensive data generated by these facilities offers opportunities for gaining competitive advantages through data-driven insights, revealing trends, patterns, and predicting future performance indicators like unplanned downtime length, which is essential in optimizing maintenance and minimizing potential losses. This paper explores statistical and machine learning techniques for modeling downtime length probability distributions and correlation with machine vibration measurements. We proposed a novel framework, employing advanced data-driven techniques like artificial neural networks (ANNs) to estimate parameters of probability distributions governing downtime lengths. Our approach specifically focuses on modeling parameters of these distribution, rather than directly modeling probability density function (PDF) values, as is common in other approaches. Experimental results indicate a significant performance boost, with the proposed method achieving up to 30% superior performance in modeling the distribution of downtime lengths compared to alternative methods. Moreover, this method facilitates unsupervised training, making it suitable for big data repositories of unlabelled data. The framework allows for potential expansion by incorporating additional input variables. In this study, machine vibration velocity measurements are selected for further investigation. The study underscores the potential of advanced data-driven techniques to enables companies to make better-informed decisions regarding their current maintenance practices and to direct improvement programs in industrial settings.https://doi.org/10.1186/s40537-024-01030-4Lean industrial systemsPaper manufacturingProduction downtimeBig data analyticsMachine learningUnsupervised learning |
| spellingShingle | Vladimir Koković Kosta Pavlović Andjela Mijanović Slavko Kovačević Ivan Mačužić Vladimir Božović Exploring statistical and machine learning methods for modeling probability distribution parameters in downtime length analysis: a paper manufacturing machine case study Journal of Big Data Lean industrial systems Paper manufacturing Production downtime Big data analytics Machine learning Unsupervised learning |
| title | Exploring statistical and machine learning methods for modeling probability distribution parameters in downtime length analysis: a paper manufacturing machine case study |
| title_full | Exploring statistical and machine learning methods for modeling probability distribution parameters in downtime length analysis: a paper manufacturing machine case study |
| title_fullStr | Exploring statistical and machine learning methods for modeling probability distribution parameters in downtime length analysis: a paper manufacturing machine case study |
| title_full_unstemmed | Exploring statistical and machine learning methods for modeling probability distribution parameters in downtime length analysis: a paper manufacturing machine case study |
| title_short | Exploring statistical and machine learning methods for modeling probability distribution parameters in downtime length analysis: a paper manufacturing machine case study |
| title_sort | exploring statistical and machine learning methods for modeling probability distribution parameters in downtime length analysis a paper manufacturing machine case study |
| topic | Lean industrial systems Paper manufacturing Production downtime Big data analytics Machine learning Unsupervised learning |
| url | https://doi.org/10.1186/s40537-024-01030-4 |
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