Remaining useful life (RUL) regression using Long–Short Term Memory (LSTM) networks

Yousuf, Sofia; Khan, Salman; Khursheed, Saqib

doi:10.1016/j.microrel.2022.114772

Cited by 5 publications

(1 citation statement)

References 16 publications

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“…The RUL is the time frame from the present until the expiration of the useful life ( Cai et al, 2022 ; Han, Li & Chen, 2023 ; Hu et al, 2023b ; Mitici et al, 2023 ; Shaheen, Kocsis & Németh, 2023 ; Wang et al, 2023 ; Yang et al, 2023 ; Yousuf, Khan & Khursheed, 2022 ; Zhu et al, 2023 ). RUL has been used in numerous industries, such as rotating equipment, batteries, and aerospace, to name a few, to warn operators of early failures ( Zhao et al, 2020b ).…”

Section: Resultsmentioning

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

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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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