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Assessment of IoT-driven predictive maintenance strategies for Computed Tomography equipment: a machine learning approach

Predictive maintenance (PdM) identifies the equipment conditions and forecasts when maintenance is required to minimize downtime, whis is crucial for medical equipment. This study developed a machine learning-based PdM for a Computed Tomography (CT) scan machine using Internet of Things (IoT) sensor...

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Main Authors: Mohd Azrul Shazril, Mohammad Habib Shah Ershad, Mashohor, Syamsiah, Amran, Mohd Effendi, Hafiz, Nur Fatinah, Ali, Azizi Mohd, Naseri, Mohd Saiful, Rasid, Mohd Fadlee A.
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers 2024
Online Access:http://psasir.upm.edu.my/id/eprint/114763/1/114763.pdf
http://psasir.upm.edu.my/id/eprint/114763/
https://ieeexplore.ieee.org/document/10804159/
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spelling my.upm.eprints.1147632025-01-31T01:07:47Z http://psasir.upm.edu.my/id/eprint/114763/ Assessment of IoT-driven predictive maintenance strategies for Computed Tomography equipment: a machine learning approach Mohd Azrul Shazril, Mohammad Habib Shah Ershad Mashohor, Syamsiah Amran, Mohd Effendi Hafiz, Nur Fatinah Ali, Azizi Mohd Naseri, Mohd Saiful Rasid, Mohd Fadlee A. Predictive maintenance (PdM) identifies the equipment conditions and forecasts when maintenance is required to minimize downtime, whis is crucial for medical equipment. This study developed a machine learning-based PdM for a Computed Tomography (CT) scan machine using Internet of Things (IoT) sensors to monitor temperature, humidity, current, radiation, and XY-axis acceleration. Data were collected from January to December 2023 at a hospital in the Klang Valley, Malaysia. The readings were preprocessed to follow a normal distribution, representing the typical working conditions of the machine. Owing to limited faulty condition data, synthetic data were generated by expanding the tails of the data distribution and using a Gaussian noise generator. These synthetic data are vital for training robust machine learning models. An artificial neural network (ANN) was designed to predict the machine's breakdown risk using all sensor parameters as inputs. The ANN model achieved an impressive prediction accuracy of 97.58%, proving its relibility in forecasting breakdowns. The model consistently predicted a high breakdown risk in November 2023, which was confirmed by a repair report that indicating maintenance was required in early December 2023. This study demonstrated that integrating IoT sensors with ANN models can significantly enhance the PdM of medical equipment, reduce downtime, and improve operational efficiency. These promising results suggest the potential application of this approach in other critical medical devices. Institute of Electrical and Electronics Engineers 2024 Article PeerReviewed text en cc_by_4 http://psasir.upm.edu.my/id/eprint/114763/1/114763.pdf Mohd Azrul Shazril, Mohammad Habib Shah Ershad and Mashohor, Syamsiah and Amran, Mohd Effendi and Hafiz, Nur Fatinah and Ali, Azizi Mohd and Naseri, Mohd Saiful and Rasid, Mohd Fadlee A. (2024) Assessment of IoT-driven predictive maintenance strategies for Computed Tomography equipment: a machine learning approach. IEEE Access, 12. pp. 195505-195515. ISSN 2169-3536; eISSN: 2169-3536 https://ieeexplore.ieee.org/document/10804159/ 10.1109/ACCESS.2024.3518516
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Predictive maintenance (PdM) identifies the equipment conditions and forecasts when maintenance is required to minimize downtime, whis is crucial for medical equipment. This study developed a machine learning-based PdM for a Computed Tomography (CT) scan machine using Internet of Things (IoT) sensors to monitor temperature, humidity, current, radiation, and XY-axis acceleration. Data were collected from January to December 2023 at a hospital in the Klang Valley, Malaysia. The readings were preprocessed to follow a normal distribution, representing the typical working conditions of the machine. Owing to limited faulty condition data, synthetic data were generated by expanding the tails of the data distribution and using a Gaussian noise generator. These synthetic data are vital for training robust machine learning models. An artificial neural network (ANN) was designed to predict the machine's breakdown risk using all sensor parameters as inputs. The ANN model achieved an impressive prediction accuracy of 97.58%, proving its relibility in forecasting breakdowns. The model consistently predicted a high breakdown risk in November 2023, which was confirmed by a repair report that indicating maintenance was required in early December 2023. This study demonstrated that integrating IoT sensors with ANN models can significantly enhance the PdM of medical equipment, reduce downtime, and improve operational efficiency. These promising results suggest the potential application of this approach in other critical medical devices.
format Article
author Mohd Azrul Shazril, Mohammad Habib Shah Ershad
Mashohor, Syamsiah
Amran, Mohd Effendi
Hafiz, Nur Fatinah
Ali, Azizi Mohd
Naseri, Mohd Saiful
Rasid, Mohd Fadlee A.
spellingShingle Mohd Azrul Shazril, Mohammad Habib Shah Ershad
Mashohor, Syamsiah
Amran, Mohd Effendi
Hafiz, Nur Fatinah
Ali, Azizi Mohd
Naseri, Mohd Saiful
Rasid, Mohd Fadlee A.
Assessment of IoT-driven predictive maintenance strategies for Computed Tomography equipment: a machine learning approach
author_facet Mohd Azrul Shazril, Mohammad Habib Shah Ershad
Mashohor, Syamsiah
Amran, Mohd Effendi
Hafiz, Nur Fatinah
Ali, Azizi Mohd
Naseri, Mohd Saiful
Rasid, Mohd Fadlee A.
author_sort Mohd Azrul Shazril, Mohammad Habib Shah Ershad
title Assessment of IoT-driven predictive maintenance strategies for Computed Tomography equipment: a machine learning approach
title_short Assessment of IoT-driven predictive maintenance strategies for Computed Tomography equipment: a machine learning approach
title_full Assessment of IoT-driven predictive maintenance strategies for Computed Tomography equipment: a machine learning approach
title_fullStr Assessment of IoT-driven predictive maintenance strategies for Computed Tomography equipment: a machine learning approach
title_full_unstemmed Assessment of IoT-driven predictive maintenance strategies for Computed Tomography equipment: a machine learning approach
title_sort assessment of iot-driven predictive maintenance strategies for computed tomography equipment: a machine learning approach
publisher Institute of Electrical and Electronics Engineers
publishDate 2024
url http://psasir.upm.edu.my/id/eprint/114763/1/114763.pdf
http://psasir.upm.edu.my/id/eprint/114763/
https://ieeexplore.ieee.org/document/10804159/
_version_ 1823093265939824640
score 13.235362

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