Digital Twin Enabled Industry 4.0 Predictive Maintenance Under Reliability-Centred Strategy

Mubarak, Akram; Asmelash, Mebrahitom; Azhari, Azmir; Alemu, Tamiru; Mulubrhan, Freselam; Saptaji, Kushendarsyah

doi:10.1109/iceeict53079.2022.9768590

Cited by 14 publications

(9 citation statements)

References 35 publications

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“…8 , the percentage of PM in previous studies was 41%. The 14 studies involved in PM are Aivaliotis et al (2023) , Aivaliotis, Georgoulias & Chryssolouris (2019) , Aivaliotis et al (2019) , Altun & Tavli (2019) , Avornu et al (2022) , Centomo, Dall’Ora & Fummi (2020) , Heim et al (2020) , Liu et al (2019) , Mubarak et al (2022) , Rajesh et al (2019) , Rossini et al (2020) , Siddiqui, Kahandawa & Hewawasam (2023) , Singh et al (2023) and Yakhni et al (2022) . PMs also have drawbacks, such as being time-consuming and expensive to destroy and restore.…”

Section: Resultsmentioning

confidence: 99%

“…Under the direst of circumstances, the machinery will be rendered inoperable, resulting in the destruction of machinery and the loss of human life ( van Dinter, Tekinerdogan & Catal, 2022 ; Zhai & Qiao, 2020 ). If the rapid intervention is not performed, the machine may encounter unforeseen failures that result in low precision, production downtime, or even catastrophic loss ( Centomo, Dall’Ora & Fummi, 2020 ; Luo et al, 2020 ; Mubarak et al, 2022 ). In the event that the concerns are not promptly remedied, there is an increased likelihood of the occurrence of catastrophic events.…”

Section: Introductionmentioning

confidence: 99%

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Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices

Abd Wahab,

Hasikin,

Wee Lai

et al. 2024

PeerJ Computer Science

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Background Maintaining machines effectively continues to be a challenge for industrial organisations, which frequently employ reactive or premeditated methods. Recent research has begun to shift its attention towards the application of Predictive Maintenance (PdM) and Digital Twins (DT) principles in order to improve maintenance processes. PdM technologies have the capacity to significantly improve profitability, safety, and sustainability in various industries. Significantly, precise equipment estimation, enabled by robust supervised learning techniques, is critical to the efficacy of PdM in conjunction with DT development. This study underscores the application of PdM and DT, exploring its transformative potential across domains demanding real-time monitoring. Specifically, it delves into emerging fields in healthcare, utilities (smart water management), and agriculture (smart farm), aligning with the latest research frontiers in these areas. Methodology Employing the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) criteria, this study highlights diverse modeling techniques shaping asset lifetime evaluation within the PdM context from 34 scholarly articles. Results The study revealed four important findings: various PdM and DT modelling techniques, their diverse approaches, predictive outcomes, and implementation of maintenance management. These findings align with the ongoing exploration of emerging applications in healthcare, utilities (smart water management), and agriculture (smart farm). In addition, it sheds light on the critical functions of PdM and DT, emphasising their extraordinary ability to drive revolutionary change in dynamic industrial challenges. The results highlight these methodologies’ flexibility and application across many industries, providing vital insights into their potential to revolutionise asset management and maintenance practice for real-time monitoring. Conclusions Therefore, this systematic review provides a current and essential resource for academics, practitioners, and policymakers to refine PdM strategies and expand the applicability of DT in diverse industrial sectors.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices

Abd Wahab,

Hasikin,

Wee Lai

et al. 2024

PeerJ Computer Science

View full text Add to dashboard Cite

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“…Based on the results of data processing with FMEA analysis, 4 critical components with the highest RPN value were obtained, namely the Bearing component with an RPN value of 560 at a very high level, because it can cause disruptions to the production line, for example when the production process is running. Suddenly, the bearing components are damaged, and the production process on the Cake Breaker Conveyor machine will stop, which can be detrimental [2]. The coupling rod has a value of RPN 512, meaning that it is at a very high level because it can cause disruption to the production line and damage the bearing.…”

Section: Discussionmentioning

confidence: 99%

“…There are no idle resources due to the breakdown of machines during the production process to minimize the cost of production losses, or these costs can be eliminated [1]. In addition, good maintenance will improve company performance, the investment value allocated to equipment and machinery can be minimized, and reasonable care can also improve the quality of products produced and reduce waste [2].…”

Section: Introductionmentioning

confidence: 99%

Cake Breaker Conveyor Machine Maintenance System Analysis Using the Reliability-Centered Maintenance (RCM) Method

Basuki Nanda,

Valentino Roni

2023

JURIT

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Machine maintenance plays a vital role in the production activities of a company, which concerns the smooth and bottleneck of production. This research was conducted in a company engaged in palm oil processing. The company often suffers from machine breakdowns and has not found a suitable solution. Reliability Maintenance is one of the methods used to determine the appropriate maintenance actions for each critical component on the Cake Breaker Conveyor (CBC) machine, based on analysis using Failure Mode and Effect Analysis (FMEA) by looking at the highest RPN value of each component, 4 critical elements are obtained from the 11 main features of the Cake Breaker Conveyor (CBC) machine, namely bearing parts, Universal Joint, Rod Clutch and Pen. Determine the maintenance schedule of critical components at the seed separation station. Taking damage, lognormal distribution, proposed replacement schedule 461.5 (hours), pen damage, normal distribution, proposed replacement schedule 587.29 (hours), universal joint damage, normal distribution, proposed replacement schedule 536.46 (hours), clutch rod damage, normal distribution, proposed replacement schedule 581.6 (hours), and axle damage, normal distribution, proposed replacement schedule 586.6 (hours).

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“…Most initiatives emphasize applications in the manufacturing industry to transform production systems into a new generation of systems that incorporate CPS and are often referred to as cyber-physical production systems. Among all of the enabling technologies of Industry 4.0, the DT is considered to lead the way for cyber-physical integration [4]; this integration is an important prerequisite for smart manufacturing as well as being its core. CPSs and DTs are the preferred means for such integration and have gained considerable attention from academia, industry, and the government.…”

Section: Introductionmentioning

confidence: 99%

An Immersive Digital Twin Applied to a Manufacturing Execution System for the Monitoring and Control of Industry 4.0 Processes

Caiza,

Sanz

2024

Applied Sciences

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The present research proposes the implementation of an architecture for industrial process monitoring and control for a manufacturing execution system (MES) using an immersive digital twin (DT). For the design of the proposal, cyber–physical systems (CPS), MES, robotics, the Internet of Things, augmented reality, virtual reality, and open platform communication-unified architecture (OPC UA) communication protocols were used to integrate these technologies and enhance the functionalities of the DT by providing greater performance. The proposed work is implemented in an Industry 4.0 laboratory that is composed of Festo Cyber–Physical Factory and CP-Lab stations. The implementation of the architecture is based on ISO 23247, where the following requirements were considered for the design of DTs: (1) observable attributes and 3D design and visualization of all physical production lines in all of their stages, (2) a communication entity through the OPC UA protocol for the collection of state changes of manufacturing elements, (3) a DT entity where digital models are modeled and updated based on the collected data, and (4) user entities through the use of AR and VR to make manufacturing more efficient. The experimental results showed that the architecture enables interoperability between different platforms and control subsystems. It allows for the detection and diagnosis of problems during the execution of the production line; in addition, the high-fidelity simulation and AR and VR environments provided by the DT with data obtained in real time can improve the accuracy and efficiency of manufacturing through a more detailed analysis of the process, providing advantages such as interactive creation for customized products and continuous innovation.

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Digital Twin Enabled Industry 4.0 Predictive Maintenance Under Reliability-Centred Strategy

Cited by 14 publications

References 35 publications

Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices

Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices

Cake Breaker Conveyor Machine Maintenance System Analysis Using the Reliability-Centered Maintenance (RCM) Method

An Immersive Digital Twin Applied to a Manufacturing Execution System for the Monitoring and Control of Industry 4.0 Processes

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