AI environment for predictive maintenance in a manufacturing scenario

Rossini, Rosaria; Prato, Gianluca; Conzon, Davide; Pastrone, Claudio; Pereira, Eurico; Reis, Joao; Gonçalves, Gil; Henriques, David; Santiago, Ana Rita; Ferreira, A.J.M.

doi:10.1109/etfa45728.2021.9613359

Cited by 7 publications

(6 citation statements)

References 14 publications

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“…Notably, this study indicated that eight studies applied ML techniques for PdM through data-driven modelling across various sectors such as aeroplane maintenance, automobile brake pads, and induction motors ( Altun & Tavli, 2019 ; Avornu et al, 2022 ; Heim et al, 2020 ; Mubarak et al, 2022 ; Rajesh et al, 2019 ; Rossini et al, 2020 ; Siddiqui, Kahandawa & Hewawasam, 2023 ; Singh et al, 2023 ). Additionally, performance prediction studies have demonstrated the potential of ML as a useful technique.…”

Section: Resultsmentioning

confidence: 99%

“…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%

“…ML techniques such as naive Bayes, random forest, support vector machine (SVM), decision tree, and regression models are the most widely used AI approaches ( Khairuddin et al, 2022 , 2023 ; Neo et al, 2022 ), artificial neural network (ANN), and decision trees ( Hosamo et al, 2022 ) and ML is also used for Autoregressive Integrated Moving Average (ARIMA) ( Yang et al, 2021 ). From the review, 19 studies use ML approaches for PdM using DT ( Altun & Tavli, 2019 ; Avornu et al, 2022 ; Das et al, 2021 ; Harries et al, 2023 ; Heim et al, 2020 ; Hosamo et al, 2022 , 2023 ; Hu et al, 2023a ; Luo et al, 2020 ; Mourtzis, Tsoubou & Angelopoulos, 2023 ; Mubarak et al, 2022 ; Panagou et al, 2022a , 2022b ; Rajesh et al, 2019 ; Rossini et al, 2020 ; Siddiqui, Kahandawa & Hewawasam, 2023 ; Singh et al, 2023 ; Wang et al, 2022 ; Werner, Zimmermann & Lentes, 2019 ).…”

Section: Resultsmentioning

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: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

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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“…The literature provides numerous examples of studies indicating the application of AI for maintenance operations in various industries and sectors: the renewable energy industry (Shin et al, 2021), manufacturing and processing of wood products (Rossini et al, 2021), the power generation industry (Allahloh et al, 2023), the automotive sector (Theissler et al, 2021;Arena et al, 2022;Katreddi et al, 2022) and particular industrial infrastructure (machinery) (Pandey et al, 2023).…”

Section: Application Of Artificial Intelligence In the Context Of Pre...mentioning

confidence: 99%

“…The Digital Twin, which can deliver additional services by leveraging physical simulation and AI algorithms, is another example of applying AI in maintenance process improvement. These services include such functions as fault diagnosis, troubleshooting, predicting the remaining useful life, and facilitating maintenance activities (Rossini et al, 2021). Application of DT solutions enabled the realtime creation and modification of workflows essential for fault diagnosis and predictive maintenance.…”

Section: Application Of Artificial Intelligence In the Context Of Pre...mentioning

confidence: 99%

Generative AI in the Manufacturing Process: Theoretical Considerations

Doanh,

Dufek,

Ejdys

et al. 2023

Engineering Management in Production and Services

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The paper aims to identify how digital transformation and Generative Artificial Intelligence (GAI), in particular, affect the manufacturing processes. Several dimensions of the Industry 4.0 field have been considered, such as the design of new products, workforce and skill optimisation, enhancing quality control, predictive maintenance, demand forecasting, and marketing strategy. The paper adopts qualitative research based on a critical review approach. It provides evidence of the GAI technology support in the mentioned areas. Appropriate use of emerging technology allows managers to transform manufacturing by optimising processes, improving product design, enhancing quality control, and contributing to overall efficiency and innovation in the industry. Simultaneously, GAI technologies facilitate predictive analytics to forecast and anticipate future demand, quality issues, and potential risks, improve a marketing strategy and identify market trends.

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