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  1. Home
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  5. Predictive Maintenance System Design for Infant Intensive Phototherapy Lamp
 
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Predictive Maintenance System Design for Infant Intensive Phototherapy Lamp

Journal
AIST 2022 - 4th International Conference on Artificial Intelligence and Speech Technology
Date Issued
2022-01-01
Author(s)
Vikneswaran Vijean
Universiti Malaysia Perlis
Razi Ahmad
Universiti Malaysia Perlis
Wan Amiza Amneera Wan Ahmad
Universiti Malaysia Perlis
Ragunathan Al Santiagoo
Universiti Malaysia Perlis
Abdul Ghapar Ahmad
Universiti Malaysia Perlis
Syakirah Afiza Mohammed
Universiti Malaysia Perlis
DOI
10.1109/AIST55798.2022.10065161
Abstract
Planned-Preventive maintenance (PPM) is an essential part of clinical engineering to ensure correct functionality of the medial equipment. PPM involves the extension of equipment's life and reducing failure by performing selective substitution of its components in contrast to the "fix it when it fails"concept. However, this strategy often leads to un-necessary downtime and increased costs, especially in hospital environment. Therefore, a maintenance system for predictive preventive maintenance that can monitor the usage of medical equipment is much preferred option. In this regards, a predictive maintenance system design is proposed that focuses on the LED Infant Intensive Phototherapy Lamp. In order to improve the weakness arise from the schedule Planned-Preventive Maintenance (PPM), the predictive maintenance system will be real time performance based in which the performance of the LED Infant Intensive Phototherapy Lamp will be monitored. The purpose of this monitoring system is to ensure that the light intensity, which is measured in irradiance level, can be delivered in sufficient amount for the baby with jaundice. In order to monitor the performances of LED infant intensive phototherapy lamp, a cloud based webpage has been implemented for real time monitoring of LED infant intensive phototherapy lamp which can be accessed by authorized personals.
Subjects
  • infant | internet of ...

File(s)
Research repository notification.pdf (4.4 MB)
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