A data mining approach to reduce the number of maintenance visits in the medical domain

Ullrich, Markus; Hagen, Klaus ten; Lässig, Jörg

doi:10.1109/idaacs.2013.6662684

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…Using the medical equipment maintenance records stored in this application's database, a mathematical model for predictive maintenance can be developed [18]. A second method was proposed by M. Ullrich, K. Ten Hagen, and J. Lässig, who described a new approach for categorizing maintenance visits according to PdM [19]. To enhance medical device decisionmaking, the following study [20] details a comprehensive PdM management system that makes use of Information and Communication Technologies (ICT) and predictive analysis tools.…”

Section: B Related Workmentioning

confidence: 99%

An Internet of Things-based Predictive Maintenance Architecture for Intensive Care Unit Ventilators

Manchadi,

BEN-BOUAZZA,

Dehbi

et al. 2024

IJACSA

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Intensive care units commonly utilize mechanical ventilators to treat patients with different medical conditions, which are crucial for patient care and survival. ICU ventilators have evolved through four distinct generations, each displaying unique features. Despite progress made since the 1940s, contemporary designs are insufficient to meet the increasing needs of patients and hospitals. Malfunctions in mechanical ventilators pose significant dangers to patients, highlighting the importance of focusing on their safety, security, precision, and dependability. Our study aims to address the significant issue at hand. Furthermore, the IoT industry has garnered significant attention because of rapid progress in smart devices, sensors, and actuators. The healthcare industry has seen a notable increase in health data as a result of the growing utilization of IoT and cloud computing technologies. To enhance growth, new models and distributed data analytics strategies must be developed to fully utilize the value of the vast datasets generated, including the incorporation of embedded machine learning. The study focuses on conducting Pareto and Failure Modes and Effects Analysis (FMECA) on ventilators in a specific hospital's ICU, specifically those manufactured by the same company and unit. The analysis aims to identify the most critical and failure-prone component. Subsequently, we propose an IoT-focused framework for a predictive maintenance system implemented at the component level. The architecture comprises a monitoring framework and a data analytics module to predict potential system failures in advance, enhancing overall reliability.

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Section: B Related Workmentioning

confidence: 99%

An Internet of Things-based Predictive Maintenance Architecture for Intensive Care Unit Ventilators

Manchadi,

BEN-BOUAZZA,

Dehbi

et al. 2024

IJACSA

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“…As an advanced maintenance, several approaches have been already proposed such as proactive maintenance [1] and predictive maintenance [2]. Proactive maintenance means repairing immediately before a customer recognizes, and predictive maintenance means repairing immediately before a failure occurs.…”

Section: Related Workmentioning

confidence: 99%

MCMC-based Efficient Maintenance Plan Decision

Shimada,

Sakajo

2017

PHMAP_CONF

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In recent years, it has been an essential policy to monitor real-time health states of facilities and determine when to perform maintenance in order to ensure the high operation atio and improve work efficiency. In this paper, target facilities diagnose their own health states by analyzing timeseries sensor data and transmit warning data and failure data to the monitoring center. These data include date and time of occurrence and warning/failure code which identifies the factor. Utilizing these data, we propose an MCMC-based maintenance plan decision to reduce the failures and the workloads. Firstly, state-based warning patterns which are composed of several warning codes are extracted. At that time, to avoid the state explosion, only warning patterns which are closely related to failure occurrence are extracted based on the time interval from warning states to failure state. Secondly, warning patterns are modeled based on N-th order Markov model. Finally, maintenance plan is decided based on failure probability. Experiment to evaluate whether the facilities can be maintained before failure occurs proved that this approach could actually reduce the number of failures and the frequency of dispatches of maintenance workers.

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Harnessing the Fourth Industrial Revolution: Trends and Challenges in Applying Industry 4.0 to Enhance Predictive Maintenance in Manufacturing, Specifically Mining

Alqaraleh,

Hajjaj,

Mohamed

2024

Lecture Notes in Networks and Systems

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A data mining approach to reduce the number of maintenance visits in the medical domain

Cited by 6 publications

References 8 publications

An Internet of Things-based Predictive Maintenance Architecture for Intensive Care Unit Ventilators

An Internet of Things-based Predictive Maintenance Architecture for Intensive Care Unit Ventilators

MCMC-based Efficient Maintenance Plan Decision

Harnessing the Fourth Industrial Revolution: Trends and Challenges in Applying Industry 4.0 to Enhance Predictive Maintenance in Manufacturing, Specifically Mining

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