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Classification model for predictive maintenance of small steam sterilisers


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


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.

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Additional Metadata

Item Type: Article
Divisions: Faculty of Computer Science and Information Technology
DOI Number: https://doi.org/10.1049/iet-cim.2019.0029
Publisher: John Wiley & Sons
Keywords: Steriliser; German; Sensor
Depositing User: Ms. Nuraida Ibrahim
Date Deposited: 18 May 2022 03:02
Last Modified: 18 May 2022 03:02
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1049/iet-cim.2019.0029
URI: http://psasir.upm.edu.my/id/eprint/88166
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