Multilayer-graph-embedded extreme learning machine for performance degradation prognosis of bearing

Shi, Mingkuan; Ding, Chuancang; Que, Hongbo; Wu, Chengpan; Shi, Juanjuan; Shen, Changqing; Huang, Weiguo; Zhu, Zhongkui

doi:10.1016/j.measurement.2022.112299

Cited by 15 publications

(4 citation statements)

References 44 publications

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“…Shi proposed a bearing life prediction method based on a multilayer graph-embedded Extreme Learning Machine (ELM). By integrating a graph embedding framework, they constructed a novel embedded graph ELM autoencoder, significantly reducing training time and improving prediction efficiency [ 6 ]. Guo introduced a method for predicting the remaining useful life of rolling bearings based on EMD-RISI-LSTM.…”

Section: Introductionmentioning

confidence: 99%

Life Prediction of Rolling Bearing Based on Optimal Time–Frequency Spectrum and DenseNet-ALSTM

Chen,

Yan,

Dong

et al. 2024

Sensors

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To address the challenges faced in the prediction of rolling bearing life, where temporal signals are affected by noise, making fault feature extraction difficult and resulting in low prediction accuracy, a method based on optimal time–frequency spectra and the DenseNet-ALSTM network is proposed. Firstly, a signal reconstruction method is introduced to enhance vibration signals. This involves using the CEEMDAN deconvolution method combined with the Teager energy operator for signal reconstruction, aiming to denoise the signals and highlight fault impacts. Subsequently, a method based on the snake optimizer (SO) is proposed to optimize the generalized S-transform (GST) time–frequency spectra of the enhanced signals, obtaining the optimal time–frequency spectra. Finally, all sample data are transformed into the optimal time–frequency spectrum set and input into the DenseNet-ALSTM network for life prediction. The comparison experiment and ablation experiment show that the proposed method has high prediction accuracy and ideal prediction performance. The optimization terms used in different contexts in this paper are due to different optimization methods, specifically the CEEMDAN method.

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Section: Introductionmentioning

confidence: 99%

Life Prediction of Rolling Bearing Based on Optimal Time–Frequency Spectrum and DenseNet-ALSTM

Chen,

Yan,

Dong

et al. 2024

Sensors

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“…Transfer learning is utilized to transfer the trained model to the wind farm to predict the main bearing life of direct-drive wind turbine. Shi et al 13 developed a multilayer-graph-embedded extreme learning machine algorithm. The structures and parameters of the models in these methods need to be manually selected, and massive samples are required, which limits the widespread application of these methods.…”

Section: Introductionmentioning

confidence: 99%

RUL prediction of rolling bearings based on adaptive variational mode decomposition and double-discriminator conditional CycleGAN

Yu,

Zhao,

Guo

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part C:

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To accurately evaluate the remaining life (RUL) of rolling bearings under small sample conditions and strong noise interference, a RUL prediction scheme using adaptive variational mode decomposition (VMD) and double-discriminator conditional CycleGAN (DD-cCycleGAN) is put forward. Combining chimp optimization algorithm (ChOA) with VMD, an adaptive VMD algorithm based on ChOA is presented, which selects effective mode components for reconstruction and reduces interference from strong background noise. A DD-cCycleGAN is developed to generate new samples which not only retain sample information of source domain, but also resemble samples of target one. A LSTM network after training is utilized to predict the bearing RUL in test samples. The performance of this scheme was validated by using the XJTU-SY bearing test dataset. The comparison analyses demonstrate this scheme has strong noise resistance and high accuracy.

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“…Because its hidden layer does not need iteration, extreme learning machine (ELM) has high learning speed and good generalization ability. It has been applied to intelligent fault diagnosis and bearing degradation prediction of rolling bearings [17,18]. However, the number of ELM hidden layer nodes will directly affect the prediction results.…”

Section: Introductionmentioning

confidence: 99%

Remaining useful life prediction method of rolling bearings based on improved 3σ and DBO-HKELM

Gao,

Li,

Zhang

et al. 2024

Meas. Sci. Technol.

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An improved 3σ method and dung beetle algorithm optimization hybrid kernel extreme learning machinebased (DBO-HKELM) approach for predicting the remaining useful life (RUL) of rolling bearings was suggested in order to increase prediction accuracy. Firstly, multi-dimensional degradation feature data is extracted from bearing vibration data. Considering the inﬂuence of noise signal on the prediction accuracy, an improved kernel principal component analysis (KPCA) method is proposed to reduce the noise of degraded features. Then, an improved 3σ method is proposed to determine the starting point of bearing degradation by combining bearing vibration signal data. Lastly, a DBO-HKELM life prediction model was put forth. The parameters of hybrid kernel extreme learning machine were optimized by dung beetle algorithm, and appropriate kernel parameters and regularization coeﬃcient were selected. The feature set of degradation indicators is input into the trained model to output the bearing remaining useful life prediction results starting from the determined degradation starting point. Multiple data sets were used to verify that the new remaining useful life prediction method signiﬁcantly improves the prediction accuracy.

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Multilayer-graph-embedded extreme learning machine for performance degradation prognosis of bearing

Cited by 15 publications

References 44 publications

Life Prediction of Rolling Bearing Based on Optimal Time–Frequency Spectrum and DenseNet-ALSTM

Life Prediction of Rolling Bearing Based on Optimal Time–Frequency Spectrum and DenseNet-ALSTM

RUL prediction of rolling bearings based on adaptive variational mode decomposition and double-discriminator conditional CycleGAN

Remaining useful life prediction method of rolling bearings based on improved 3σ and DBO-HKELM

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