Data Amplification for Bearing Remaining Useful Life Prediction Based on Generative Adversarial Network

Lei, Luyao; Li, Xiang; Wen, Juan; Miao, Junhao

doi:10.1155/2022/4628462

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…These methods search and evaluate the parameter space to find the optimal combination of parameters, optimizing the performance of the model. Common ML algorithms used for this task include linear regression, support vector machines, decision trees, random forests, gradient boosting trees, and neural networks [107][108][109][110]. These algorithms can be selected and adjusted based on the characteristics of the data and the requirements of the task to obtain accurate and reliable RUL prediction models.…”

Section: Supervised ML For Remaining Life Predictionmentioning

confidence: 99%

Advancements in bearing remaining useful life prediction methods: a comprehensive review

Song,

Lin,

Jin

et al. 2024

Meas. Sci. Technol.

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This paper presents a comprehensive review of the state-of-the-art techniques for predicting the remaining useful life (RUL) of rolling bearings. Four key aspects of bearing RUL prediction are considered: data acquiring, construction of health indicators (HI), development of RUL prediction algorithms, and evaluation of prediction results. Additionally, publicly available datasets that can be used to validate bearing prediction algorithms are described. The existing RUL prediction algorithms are categorized into three types and have been comprehensively reviewed: physical-based, statistical-based, and data-driven. In particular, the progress made in data-driven prediction methods is summarized, and typical methods such as RNN, TCN, GCN, Transform, and TL-based methods are introduced in detail. Finally, the challenges faced by data-driven methods in RUL prediction for bearings are discussed.

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Section: Supervised ML For Remaining Life Predictionmentioning

confidence: 99%

Advancements in bearing remaining useful life prediction methods: a comprehensive review

Song,

Lin,

Jin

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Rolling bearing as a * Authors to whom any correspondence should be addressed. key component of rotating machinery, is widely used in transport, manufacturing, military, and other fields of equipment [3,4]. Therefore, the working condition of rotating machinery is closely related to rolling bearings.…”

Section: Introductionmentioning

confidence: 99%

RUL prediction method for rolling bearing using convolutional denoising autoencoder and bidirectional LSTM

Yao,

Zhu,

Jiang

et al. 2023

Meas. Sci. Technol.

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Remaining useful life (RUL) prediction of rolling bearing plays an important role in maintaining the safety of the equipment. However, the data collected from industrial scene often contains noises, which affects the RUL prediction precision of rolling bearing. To overcome the above problem, a data-driven scheme for RUL prediction of rolling bearing is proposed based on convolutional denoising autoencoder (CDAE) and bidirectional long short-term memory network (Bi-LSTM). In the proposed method, the vibration signal is directly used as input of the prognostics network model. Then, a denoising network model based on CDAE is built to reduce the effect of noise. Through stacking the convolutional autoencoder, the noise component is automatically removed from the raw data. Finally, the network model based on Bi-LSTM is established to extract the high-dimensional degradation characteristics of bearing and estimate the RUL of the rolling bearing. The experimental results on the Xi’an Jiaotong University bearing dataset show that the proposed method has satisfied performance of RUL prediction.

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Generative adversarial networks for prognostic and health management of industrial systems: A review

Li,

Tang,

Chu

2024

Expert Systems with Applications

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Data Amplification for Bearing Remaining Useful Life Prediction Based on Generative Adversarial Network

Cited by 5 publications

References 13 publications

Advancements in bearing remaining useful life prediction methods: a comprehensive review

Advancements in bearing remaining useful life prediction methods: a comprehensive review

RUL prediction method for rolling bearing using convolutional denoising autoencoder and bidirectional LSTM

Generative adversarial networks for prognostic and health management of industrial systems: A review

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