Digital twin-driven intelligent assessment of gear surface degradation

Feng, Ke; Ji, Jinchen; Zhang, Yongchao; Ni, Qing; Liu, Zheng; Beer, Michael

doi:10.1016/j.ymssp.2022.109896

Cited by 198 publications

(45 citation statements)

References 47 publications

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“…Therefore, it would be better to place more effort into failure mechanism modeling for the compound fault of complex mechanical systems. Fortunately, in recent years, the technology of the digital twin, which aims to build a dynamic virtual copy of a physical system, process, or environment that behaves identically to its real-world counterpart, has attracted growing attention from researchers in the related field [146]. We believe that, in the near future, it would be a promising tool to solve the problems mentioned above.…”

Section: A Failure Mechanism Modeling For Compound Fault Of Complex M...mentioning

confidence: 99%

Compound Fault Diagnosis for Rotating Machinery: State-of-the-Art, Challenges, and Opportunities

Huang

Xia

Zhang

et al. 2023

JDMD

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Compound fault, as a primary failure leading to unexpected downtime of rotating machinery, dramatically increases the difficulty in fault diagnosis. To deal with the difficulty encountered in implementing compound fault diagnosis (CFD), researchers and engineers from industry and academia have made numerous significant breakthroughs in recent years. Admittedly, many systematic surveys focused on fault diagnosis have been conducted by reputable researchers. Nevertheless, previous review articles paid more attention to fault diagnosis with several single or independent faults, resulting in that there is still lacking a comprehensive survey on CFD. Therefore, to fulfill the above requirements, it is necessary to provide an in-depth overview of fault diagnosis methods or algorithms for compound faults of rotating machinery and uncover potential challenges or opportunities that would guide and inspire readers to devote their efforts to promoting fault diagnosis technology more effective and practical. Specifically, the backgrounds including the related definitions and a new taxonomy of CFD methods are detailed according to the way of implementing compound fault recognition. Then, the state-of-the-art applications of CFD are overviewed based on relevant publications in the past decades. Finally, the challenges and opportunities associated with implementing CFD are concluded and followed by a conclusion for ending this survey. We believe that this review article can provide a systematic guideline of CFD from different aspects for potential readers and seasoned researchers.

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Section: A Failure Mechanism Modeling For Compound Fault Of Complex M...mentioning

confidence: 99%

Compound Fault Diagnosis for Rotating Machinery: State-of-the-Art, Challenges, and Opportunities

Huang

Xia

Zhang

et al. 2023

JDMD

View full text Add to dashboard Cite

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“…[5][6][7] In recent years, the issue of equipment lifetime has become increasingly complex due to the highly non-linear and complex nature of mechanical systems. 8 Prognostics and health management (PHM) have received much attention as they can assess the system status by monitoring and analyzing data and evaluating its RUL, [9][10][11][12] thus significantly improving the efficiency of operation and maintenance. The main methods used to predict the RUL of rolling bearings are model-driven, data-driven and hybrid-based methods.…”

Section: Introductionmentioning

confidence: 99%

A CNN‐BiLSTM‐Bootstrap integrated method for remaining useful life prediction of rolling bearings

Guo

Wang

et al. 2023

Quality & Reliability Eng

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Rolling bearings, an essential fundamental component in machinery and equipment, have been widely used. Predicting the remaining useful life (RUL) of rolling bearings helps maintain the reliability of mechanical systems. Accurate prediction of RUL requires extracting deep features in complex non-linear vibration signals, the prediction results often vary widely. This paper proposes a RUL prediction method based on convolutional neural network (CNN), bi-directional long-short term memory (BiLSTM), and bootstrap method (CNN-BiLSTM-Bootstrap) to model the forecasting uncertainty. The first step is to extract the first prediction time (FPT) of the degradation phase of rolling bearings using an adaptive method for the 3𝜎 intervals of rolling bearing vibration signal kurtosis. The model extracts the spatio-temporal features through CNN and BiLSTM, and combines the bootstrap method to quantify the RUL prediction interval (PI) of rolling bearings. The comparison with existing models verified the effectiveness and generalization of the proposed model.

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“…The results showed that the network has better performance in training speed. Related research can be accessed at 26–32 …”

Section: Introductionmentioning

confidence: 99%

Remaining useful life prediction of rolling bearings based on Bayesian neural network and uncertainty quantification

Jiang

Yang

Cheng

et al. 2023

Quality & Reliability Eng

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This paper constructs a remaining useful life (RUL) prediction model combining a convolutional neural network and a long short-term memory network (CNNL-STM) to support decision-making, especially the safety of rotational equipment. It avoids the influence of personnel and realizes the complementary advantages of the network. With the assistance of Bayesian short-term and long-term memory neural networks, the remaining life prediction method is able to provide the confidence interval of the remaining life prediction of rolling bearings. The compression between the proposed method and existing state-of-the-art methods validated the good performance of the proposed method. Overall, the proposed method contributes to life prediction and condition-based maintenance of bearings and complex rotational systems.

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Digital twin-driven intelligent assessment of gear surface degradation

Cited by 198 publications

References 47 publications

Compound Fault Diagnosis for Rotating Machinery: State-of-the-Art, Challenges, and Opportunities

Compound Fault Diagnosis for Rotating Machinery: State-of-the-Art, Challenges, and Opportunities

A CNN‐BiLSTM‐Bootstrap integrated method for remaining useful life prediction of rolling bearings

Remaining useful life prediction of rolling bearings based on Bayesian neural network and uncertainty quantification

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