Data-driven failure prediction and RUL estimation of mechanical components using accumulative artificial neural networks

Shaheen, Basheer; Kocsis, Ádám; Németh, István

doi:10.1016/j.engappai.2022.105749

Cited by 15 publications

(2 citation statements)

References 44 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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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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“…However, traditional maintenance policies are incapable of responding promptly and accurately to the changes of system states that meet the demand for "RAMS" services. Therefore, with the support of realtime data acquisition and processing technologies, Predictive Health Management (PHM) solutions represented by Predictive Maintenance (PdM) has gradually become a current research hotspot under the industry 4.0 paradigm [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Predictive Maintenance Policy Based on Dynamic Probability Distribution Prediction of Remaining Useful Life

Xie,

Xue,

Zhang

et al. 2023

Machines

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As the reliability, availability, maintainability, and safety of industrial equipment have become crucial in the context of intelligent manufacturing, there are increasing expectations and requirements for maintenance policies. Compared with traditional methods, data-driven Predictive Maintenance (PdM), a superior approach to equipment and system maintenance, has been paid considerable attention by scholars in this field due to its high applicability and accuracy with a highly reliable quantization basis provided by big data. However, current data-driven methods typically provide only point estimates of the state rather than quantification of uncertainty, impeding effective maintenance decision-making. In addition, few studies have conducted further research on maintenance decision-making based on state predictions to achieve the full functionality of PdM. A PdM policy is proposed in this work to obtain the continuous probability distribution of system states dynamically and make maintenance decisions. The policy utilizes the Long Short-Term Memory (LSTM) network and Kernel Density Estimation with a Single Globally-optimized Bandwidth (KDE-SGB) method to dynamic predicting of the continuous probability distribution of the Remaining Useful Life (RUL). A comprehensive optimization target is introduced to establish the maintenance decision-making approach acquiring recommended maintenance time. Finally, the proposed policy is validated through a bearing case study, indicating that it allows for obtaining the continuous probability distribution of RUL centralized over a range of ±10 sampling cycles. In comparison to the other two policies, it could reduce the maintenance costs by 24.49~70.02%, raise the availability by 0.46~1.90%, heighten the reliability by 0.00~27.50%, and promote more stable performance with various maintenance cost and duration. The policy has offered a new approach without priori hypotheses for RUL prediction and its uncertainty quantification and provided a reference for constructing a complete PdM policy integrating RUL prediction with maintenance decision-making.

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Automl-Based Predictive Maintenance Model for Accurate Failure Detection

Cesur,

Duymaz

2023

Lecture Notes in Mechanical Engineering

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Data-driven failure prediction and RUL estimation of mechanical components using accumulative artificial neural networks

Cited by 15 publications

References 44 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

Data-Driven Predictive Maintenance Policy Based on Dynamic Probability Distribution Prediction of Remaining Useful Life

Automl-Based Predictive Maintenance Model for Accurate Failure Detection

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