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Optimizing Energy-Centered Maintenance for Medical Devices in Hospital Using K-NN Classification from Its Residual Current.

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Title: Optimizing Energy-Centered Maintenance for Medical Devices in Hospital Using K-NN Classification from Its Residual Current.
Authors: Sutanto, Erwin1,2 (AUTHOR) irfansaputra99879951@gmail.com, Saputra, Muhammad Irfan1,2 (AUTHOR), Escrivá-Escrivá, Guillermo3 (AUTHOR), Chandra Satria Arisgraha, Franky1,4 (AUTHOR), Rudyardjo, Djony Izak4 (AUTHOR), Rusydi, Febdian2,4 (AUTHOR)
Source: Energies (19961073). May2026, Vol. 19 Issue 10, p2309. 21p.
Subject Terms: *K-nearest neighbor classification, *Condition-based maintenance, *Signal processing, *Medical equipment, *Health facilities, *Machine learning, *Stray currents
Abstract: Regular time-based preventive maintenance for medical devices often fails to detect actual component degradation. This study proposes a K-NN predictive framework that analyzes the residual current signal ( I Δ ) to categorize the operational conditions of medical devices across two representative device types: Syringe Pump and Patient Monitor. The raw signal was transformed into a higher-dimensional feature space, consisting of mean, standard deviation, gap, and RMS, to handle its characteristics. By evaluating various distance metrics, the results show that Cosine provides the most efficient diagnostic path, achieving optimal factor ( f o p t ) at a lower number of neighbor parameters, at K = 4 for Syringe Pump and K = 8 for Patient Monitor with an accuracy of 94.21% and 94.41%, respectively. The disparity in K-values reflects the inherent model complexity resulting from distinct power supply architectures, a characteristic also manifested in reactive power (Q). By mapping this statistical transformation, the model overcomes the limitations of static threshold-based leakage current monitoring. This research marks a paradigm shift towards a data-driven Energy-Centered Maintenance (ECM) strategy. By facilitating interventions triggered by empirically assessed signal signatures rather than predetermined time intervals, this framework optimizes maintenance activities and enhances the overall energy efficiency of hospital infrastructure. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 194141424
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Optimizing Energy-Centered Maintenance for Medical Devices in Hospital Using K-NN Classification from Its Residual Current.
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  Data: <searchLink fieldCode="AR" term="%22Sutanto%2C+Erwin%22">Sutanto, Erwin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> irfansaputra99879951@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Saputra%2C+Muhammad+Irfan%22">Saputra, Muhammad Irfan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Escrivá-Escrivá%2C+Guillermo%22">Escrivá-Escrivá, Guillermo</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chandra+Satria+Arisgraha%2C+Franky%22">Chandra Satria Arisgraha, Franky</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rudyardjo%2C+Djony+Izak%22">Rudyardjo, Djony Izak</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rusydi%2C+Febdian%22">Rusydi, Febdian</searchLink><relatesTo>2,4</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. May2026, Vol. 19 Issue 10, p2309. 21p.
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  Data: *<searchLink fieldCode="DE" term="%22K-nearest+neighbor+classification%22">K-nearest neighbor classification</searchLink><br />*<searchLink fieldCode="DE" term="%22Condition-based+maintenance%22">Condition-based maintenance</searchLink><br />*<searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br />*<searchLink fieldCode="DE" term="%22Medical+equipment%22">Medical equipment</searchLink><br />*<searchLink fieldCode="DE" term="%22Health+facilities%22">Health facilities</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Stray+currents%22">Stray currents</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Regular time-based preventive maintenance for medical devices often fails to detect actual component degradation. This study proposes a K-NN predictive framework that analyzes the residual current signal ( I Δ ) to categorize the operational conditions of medical devices across two representative device types: Syringe Pump and Patient Monitor. The raw signal was transformed into a higher-dimensional feature space, consisting of mean, standard deviation, gap, and RMS, to handle its characteristics. By evaluating various distance metrics, the results show that Cosine provides the most efficient diagnostic path, achieving optimal factor ( f o p t ) at a lower number of neighbor parameters, at K = 4 for Syringe Pump and K = 8 for Patient Monitor with an accuracy of 94.21% and 94.41%, respectively. The disparity in K-values reflects the inherent model complexity resulting from distinct power supply architectures, a characteristic also manifested in reactive power (Q). By mapping this statistical transformation, the model overcomes the limitations of static threshold-based leakage current monitoring. This research marks a paradigm shift towards a data-driven Energy-Centered Maintenance (ECM) strategy. By facilitating interventions triggered by empirically assessed signal signatures rather than predetermined time intervals, this framework optimizes maintenance activities and enhances the overall energy efficiency of hospital infrastructure. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194141424
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        Value: 10.3390/en19102309
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      – Code: eng
        Text: English
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        PageCount: 21
        StartPage: 2309
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      – SubjectFull: K-nearest neighbor classification
        Type: general
      – SubjectFull: Condition-based maintenance
        Type: general
      – SubjectFull: Signal processing
        Type: general
      – SubjectFull: Medical equipment
        Type: general
      – SubjectFull: Health facilities
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Stray currents
        Type: general
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      – TitleFull: Optimizing Energy-Centered Maintenance for Medical Devices in Hospital Using K-NN Classification from Its Residual Current.
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            NameFull: Sutanto, Erwin
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            NameFull: Saputra, Muhammad Irfan
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            NameFull: Escrivá-Escrivá, Guillermo
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            NameFull: Rudyardjo, Djony Izak
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            – D: 15
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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              Value: 19
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            – TitleFull: Energies (19961073)
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