Predictive Maintenance in Healthcare System: A Survey

Manchadi, Oumaima; Ben-Bouazza, Fatima-Ezzahraa; Jioudi, Bassma

doi:10.1109/access.2023.3287490

Cited by 9 publications

(1 citation statement)

References 44 publications

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“…Under the fixed-schedule maintenance mode, if the equipment suddenly fails, it often causes a great influence such as the wasting of manpower and the increase in costs. In another way, predictive maintenance (PdM) extracts the crucial features from the physical parameters to produce a health indicator through a modelling and validation process [8][9][10][11]. In general, the typical parameters of manufacturing automation equipment include the vibration, sound noise, temperature, voltage and current, etc.…”

Section: Introductionmentioning

confidence: 99%

A Robot-Operation-System-Based Smart Machine Box and Its Application on Predictive Maintenance

Chang,

Chai,

et al. 2023

Sensors

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Predictive maintenance is a proactive approach to maintenance in which equipment and machinery are monitored and analyzed to predict when maintenance is needed. Instead of relying on fixed schedules or reacting to breakdowns, predictive maintenance uses data and analytics to determine the appropriate time to perform maintenance activities. In industrial applications, machine boxes can be used to collect and transmit the feature information of manufacturing machines. The collected data are essential to identify the status of working machines. This paper investigates the design and implementation of a machine box based on the ROS framework. Several types of communication interfaces are included that can be adopted to different sensor modules for data sensing. The collected data are used for the application on predictive maintenance. The key concepts of predictive maintenance include data collection, a feature analysis, and predictive models. A correlation analysis is crucial in a feature analysis, where the dominant features can be determined. In this work, linear regression, a neural network, and a decision tree are adopted for model learning. Experimental results illustrate the feasibility of the proposed smart machine box. Also, the remaining useful life can be effectively predicted according to the trained models.

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Section: Introductionmentioning

confidence: 99%

A Robot-Operation-System-Based Smart Machine Box and Its Application on Predictive Maintenance

Chang,

Chai,

et al. 2023

Sensors

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Multiple tasks control of nonlinear systems under signal temporal logic and its application to mobile robots

Wu,

Long

2024

Intl J Robust & Nonlinear

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This paper addresses the control problem of complex temporal logic tasks for nonlinear systems. A new control barrier function (CBF) called time‐varying extent‐compatible control barrier function (TV‐ECCBF) is proposed, which extends the traditional CBF by considering time‐varying extent or volumes. The main aim of TV‐ECCBF is to tackle the issue of avoiding obstacles in real‐world situations that have irregular or dynamically changing shapes, which cannot be efficiently resolved through traditional CBF methods. To encode complex task specifications, a control framework is presented based on signal temporal logic (STL). Additionally, we introduce robust semantics to discuss the satisfaction of target formulas. Moreover, the control framework generates controllers automatically based on different types of tasks, exhibiting global performance in achieving complex tasks. Finally, two examples of mobile robot systems are presented to demonstrate the effectiveness of the TV‐ECCBF and the proposed design framework.

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