A bidirectional recursive gated dual attention unit based RUL prediction approach

Yang, Lei; Liao, Yuhe; Duan, Rongkai; Kang, Tao; Xue, Jiutao

doi:10.1016/j.engappai.2023.105885

Cited by 25 publications

(4 citation statements)

References 37 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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“…Therefore, the accurate delineation of the first prediction time (FPT) of rolling bearings has become a key concern [15,16], and more existing methods for determining the FPT are mainly based on the 3σ guidelines [17,18]. Yang et al [19] proposed a continuous gradient identification algorithm to identify the initial degradation time. Ahmad et al [20] used the alarm boundary technique to determine the start prediction time.…”

Section: Introductionmentioning

confidence: 99%

Remaining life prediction of rolling bearings with secondary feature selection and BSBiLSTM

Song,

Wang,

Liu

et al. 2024

Meas. Sci. Technol.

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Rolling element bearings are critical components in rotating machinery. To tackle the problem of difficult to accurately characterize the operating state of rolling bearings caused by irrelevance and varying sensitivity of multiple features to performance degradation, and introduction of subjective errors in determination of hyperparameters of deep learning models, which can affect the accuracy and efficiency of RUL prediction. To address these challenges, this paper proposed a novel RUL prediction method for rolling bearings with secondary feature selection and BSBiLSTM. Firstly, Multi-domain features are extracted from noise-reduced vibration signals. Then, a three-criterion constraint-based feature selection algorithm is used and a secondary selection algorithm with Pearson correlation coefficient is proposed to improve data quality. Next, the 3σ criterion is integrated to determine the first prediction time for rolling bearings and to divide the degradation stage. Subsequently, the BiLSTM model with Bayesian optimization and self-attention mechanism is proposed to predict the RUL of rolling bearings to further improve the algorithm efficiency. Experimental results on PRONOSTIA platform dataset demonstrate that the proposed method outperforms many existing mainstream RUL prediction techniques, exhibiting superior prediction accuracy.

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“…They used a relevance vector machine to distinguish the degradation state of bearings into no obvious fault tendency and fault tendency, and then used a deep separable convolutional gated recursive network for feature extraction of sensor data. Yang et al [28] proposed a continuous gradient recognition algorithm to identify the initial degradation time and divide the entire health degradation process into normal operation stage and accelerated degradation stage. Then used a bidirectional recursive gated dual attention unit to extract the sensor degradation features of bearings in the accelerated degradation stage and carried out RUL prediction.…”

Section: Introductionmentioning

confidence: 99%

Operation stage division and RUL prediction of bearings based on 1DCNN-ON-LSTM

Guo,

Li,

Huang

2023

Meas. Sci. Technol.

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Remaining useful life (RUL) prediction of bearings is significantly important to ensure reliable operation of bearings. In practice, it is routinely impossible to obtain the full life cycle degradation data of bearings that needs to be used in prediction. The accuracy of the RUL prediction of bearings is often affected by incomplete degradation data. Regarding this situation, this paper proposes a multi-sensor three-stage RUL prediction framework based on the one-dimensional convolutional ordered neuron long short-term memory (1DCNN-ON-LSTM) neural network. Firstly, 1DCNN is used to extract spatial features adaptively from multi-sensor’s data and fuse them into one-dimensional feature. Next, the unsupervised hierarchy mechanism of time series information based ON-LSTM is developed to determine the ‘initial degradation stage point’ and ‘rapid degradation stage point’ of the bearing from the one-dimensional feature. Once the signal features collected by sensors input to the model reach the degradation stage point, select the corresponding sensitive features as input and construct the 1DCNN-ON-LSTM model that performs RUL prediction after the degradation stage point to improve the prediction accuracy of the model. Based on the proposed hierarchy mechanism, the bearings’ operation process is divided into three operation stages: normal stage, initial degradation stage and rapid degradation stage. Finally, the experiments verify that the proposed method can effectively divide the operation stages of bearings to predict the RUL and improve the generalization ability and prediction accuracy of the model.

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A bidirectional recursive gated dual attention unit based RUL prediction approach

Cited by 25 publications

References 37 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

Remaining life prediction of rolling bearings with secondary feature selection and BSBiLSTM

Operation stage division and RUL prediction of bearings based on 1DCNN-ON-LSTM

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