Classification model for predictive maintenance of small steam sterilisers

• Published in: IET collaborative intelligent manufacturing 2020-03, Vol.2 (1), p.1-13
• Main Authors: Musabayli, Musagil, Osman, Mohd Hafeez, Dirix, Michael
• Format: Article
• Language: English
• Subjects: after‐sales service; Algorithms; autoclaves; Boilers; CertoClav Vacuum Pro steam sterilisers; Classification; Critical components; failure analysis; Failure detection; failure probability; Feature selection; Laboratories; Maintenance costs; Methods; predictive failure detection mechanism; Predictive maintenance; predictive maintenance mechanism; Preventive maintenance; random forest algorithm; safety; safety risk; Sensors; steam generator data sets; Sterilizers; Temperature; vacuum pump; Vacuum pumps
• ISSN: 2516-8398; 2516-8398
• DOI: 10.1049/iet-cim.2019.0029

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.