Industry 4.0 Technologies for Maintenance Management – An Overview

Jasiulewicz-Kaczmarek, Małgorzata; Antosz, Katarzyna

doi:10.1007/978-3-031-09382-1_7

Cited by 16 publications

(24 citation statements)

References 69 publications

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“…Maintenance shifts from corrective and preventive to predictive and proactive, and links intelligent devices with all stakeholders’ applications and databases ( 100 , 112 ). Maintenance 4.0 is a sub-area of Industry 4.0 ( 112 ), and research shows that significant innovations in maintenance are driven mainly by data ( 113 ). Relevant and quality big data provide prospects for predicting asset failures, leading to better decision-making and maintenance performance ( 113 ).…”

Section: The Future Of Gravel Road Maintenancementioning

confidence: 99%

“…Maintenance 4.0 is a sub-area of Industry 4.0 ( 112 ), and research shows that significant innovations in maintenance are driven mainly by data ( 113 ). Relevant and quality big data provide prospects for predicting asset failures, leading to better decision-making and maintenance performance ( 113 ). A digitalized maintenance system helps reduce the risk of failure.…”

Section: The Future Of Gravel Road Maintenancementioning

confidence: 99%

“…Moreover, visualizing the analyzed results is essential in interpreting big data with innovative approaches, such as augmented reality (AR) and digital twins, which combine real world and virtual objects to predict the remaining useful life of a physical asset using physics-based models and data-driven analytics ( 113 ). Failure probability prediction can ascertain the time to failure of an asset and the associated risks ( 115 ).…”

Section: The Future Of Gravel Road Maintenancementioning

confidence: 99%

“…Condition monitoring is a prerequisite for a DDM, encompassing data acquisition, processing, analysis, interpretation, and information extraction to identify deviations in asset health ( 116 ). Knowledge from both maintenance and data science is required to develop a DDM for maintenance ( 113 ). A DDM can be developed using ML algorithms and visual analytics by mapping the monitored data to the physical characteristics of possible failures ( 111 ).…”

Section: The Future Of Gravel Road Maintenancementioning

confidence: 99%

See 3 more Smart Citations

Literature Review on Gravel Road Maintenance: Current State and Directions for Future Research

Mbiyana

Kans

Campos

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Gravel roads form a significant share of the global road network, usually in sparsely populated rural areas. They are important, especially in agriculture, tourism, and forestry, connecting rural to urban areas. This systematic literature study comprises 105 reviewed publications on gravel road maintenance. Review articles on maintenance management practices, especially concerning objective condition assessment and data-driven methods (DDMs), are lacking. Therefore, this review provides a concise overview of current gravel road maintenance practices and ongoing research on objective condition assessment and DDMs for gravel road maintenance. It offers researchers in gravel road maintenance and other related fields a clear indication of where to focus their research efforts, as it suggests the direction for future research. Visual assessment methods are predominant for monitoring the condition of gravel roads, while objective methods and DDMs are not common. Research on gravel roads and their maintenance has increased in the last two decades, especially in North America and Northern Europe. Condition assessment is shifting from subjective to objective methods, utilizing knowledge from technological advancements in image processing, vibration and acoustics analysis, and so forth. There are some excellent research initiatives for objectively assessing the condition of gravel roads and DDMs, but the practical implementation is limited. Implementing objective assessment methods and DDMs generally improves the management of gravel roads with regard to decision-making, maintenance costs, safety, and the stability and comfort of the ride. Objective condition assessment and DDMs have the potential to enhance maintenance practices in the maintenance of gravel roads.

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Section: The Future Of Gravel Road Maintenancementioning

confidence: 99%

Section: The Future Of Gravel Road Maintenancementioning

confidence: 99%

Section: The Future Of Gravel Road Maintenancementioning

confidence: 99%

Section: The Future Of Gravel Road Maintenancementioning

confidence: 99%

See 2 more Smart Citations

Literature Review on Gravel Road Maintenance: Current State and Directions for Future Research

Mbiyana

Kans

Campos

et al. 2022

Transportation Research Record: Journal of the Transportation R

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“…Predictive maintenance is an often adopted approach for mitigating downtime of automated production systems by monitoring the condition of parts of the system to perform maintenance when it is most cost-effective [ 1 ]. Predictive monitoring on the other hand focuses on optimizing the production in real time, based on the monitored parameters in industrial applications [ 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

Predictive Monitoring System for Autonomous Mobile Robots Battery Management Using the Industrial Internet of Things Technology

et al. 2022

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The core of the research focuses on analyzing the discharge characteristic of a lithium NMC battery in an autonomous mobile robot, which can be used as a model to predict its future states depending on the amount of missions queued. In the presented practical example, an autonomous mobile robot is used for in-house transportation, where its missions are queued or delegated to other robots in the system depending on the robots’ predicted state of charge. The system with the implemented models has been tested in three scenarios, simulating real-life use cases, and has been examined in the context of the number of missions executed in total. The main finding of the research is that the battery discharge characteristic stays consistent regardless of the mission type or length, making it usable as a model for the predictive monitoring system, which allows for detection of obstruction of the default shortest paths for the programmed missions. The model is used to aid the maintenance department with information on any anomalies detected in the robot’s path or the behavior of the battery, making the transportation process safer and more efficient by alerting the employees to take action or delegate the excessive tasks to other robots.

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Maintenance 4.0 research landscape: Insights and key trends from a bibliometric network analysis

Saihi,

Ben-Daya,

As’ad

2024

International Conference on Electronics, Engineering Physics, and Earth Science

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Industry 4.0 Technologies for Maintenance Management – An Overview

Cited by 16 publications

References 69 publications

Literature Review on Gravel Road Maintenance: Current State and Directions for Future Research

Literature Review on Gravel Road Maintenance: Current State and Directions for Future Research

Predictive Monitoring System for Autonomous Mobile Robots Battery Management Using the Industrial Internet of Things Technology

Maintenance 4.0 research landscape: Insights and key trends from a bibliometric network analysis

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