Classification model for predictive maintenance of small steam sterilisers
With 35,000 small steam sterilisers in the German market, after-sales service and maintenance are critical issues for manufacturers and distributors. At present, preventive maintenance is one of the most commonly-implemented maintenance strategies. However, with an average failure probability of 10%...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
John Wiley & Sons
2020
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| Online Access: | http://psasir.upm.edu.my/id/eprint/88166/ http://psasir.upm.edu.my/id/eprint/88166/1/ABSTRACT.pdf |
| _version_ | 1848860568449974272 |
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| author | Musabayli, Musagil Osman, Mohd Hafeez Dirix, Michael |
| author_facet | Musabayli, Musagil Osman, Mohd Hafeez Dirix, Michael |
| author_sort | Musabayli, Musagil |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | With 35,000 small steam sterilisers in the German market, after-sales service and maintenance are critical issues for manufacturers and distributors. At present, preventive maintenance is one of the most commonly-implemented maintenance strategies. However, with an average failure probability of 10%, ∼3500 autoclaves require unplanned repair per year, causing customers’ business interruptions and increased maintenance costs. From the authors’ observation, a predictive failure detection mechanism is needed to prevent failures and reduce the significant safety risk. Hence, this study proposes a predictive maintenance mechanism for small steam sterilisers. The predictive maintenance mechanism is constructed from classification models that categorised the health condition of two critical components in small steam sterilisers, i.e. a vacuum pump and a steam generator. The classification models were built from multisensory data, obtained from 1000 protocol records of CertoClav Vacuum Pro steam sterilisers. They perform exploratory experiments to find a suitable classification model. This study found that the random forest algorithm performed best in terms of accuracy for both the vacuum pump and steam generator data sets (83.5 and 82.0%, respectively). They also found that the features related to the pre-vacuum stage profoundly influence the condition of the vacuum pump and the steam generator. |
| first_indexed | 2025-11-15T12:47:18Z |
| format | Article |
| id | upm-88166 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T12:47:18Z |
| publishDate | 2020 |
| publisher | John Wiley & Sons |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-881662022-05-18T03:02:40Z http://psasir.upm.edu.my/id/eprint/88166/ Classification model for predictive maintenance of small steam sterilisers Musabayli, Musagil Osman, Mohd Hafeez Dirix, Michael With 35,000 small steam sterilisers in the German market, after-sales service and maintenance are critical issues for manufacturers and distributors. At present, preventive maintenance is one of the most commonly-implemented maintenance strategies. However, with an average failure probability of 10%, ∼3500 autoclaves require unplanned repair per year, causing customers’ business interruptions and increased maintenance costs. From the authors’ observation, a predictive failure detection mechanism is needed to prevent failures and reduce the significant safety risk. Hence, this study proposes a predictive maintenance mechanism for small steam sterilisers. The predictive maintenance mechanism is constructed from classification models that categorised the health condition of two critical components in small steam sterilisers, i.e. a vacuum pump and a steam generator. The classification models were built from multisensory data, obtained from 1000 protocol records of CertoClav Vacuum Pro steam sterilisers. They perform exploratory experiments to find a suitable classification model. This study found that the random forest algorithm performed best in terms of accuracy for both the vacuum pump and steam generator data sets (83.5 and 82.0%, respectively). They also found that the features related to the pre-vacuum stage profoundly influence the condition of the vacuum pump and the steam generator. John Wiley & Sons 2020 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/88166/1/ABSTRACT.pdf Musabayli, Musagil and Osman, Mohd Hafeez and Dirix, Michael (2020) Classification model for predictive maintenance of small steam sterilisers. IET Collaborative Intelligent Manufacturing, 2 (1). pp. 1-13. ISSN 2516-8398 https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-cim.2019.0029 10.1049/iet-cim.2019.0029 |
| spellingShingle | Musabayli, Musagil Osman, Mohd Hafeez Dirix, Michael Classification model for predictive maintenance of small steam sterilisers |
| title | Classification model for predictive maintenance of small steam sterilisers |
| title_full | Classification model for predictive maintenance of small steam sterilisers |
| title_fullStr | Classification model for predictive maintenance of small steam sterilisers |
| title_full_unstemmed | Classification model for predictive maintenance of small steam sterilisers |
| title_short | Classification model for predictive maintenance of small steam sterilisers |
| title_sort | classification model for predictive maintenance of small steam sterilisers |
| url | http://psasir.upm.edu.my/id/eprint/88166/ http://psasir.upm.edu.my/id/eprint/88166/ http://psasir.upm.edu.my/id/eprint/88166/ http://psasir.upm.edu.my/id/eprint/88166/1/ABSTRACT.pdf |