Remaining Useful Life Prediction for Bearings Based on a Gated Recurrent Unit

Que, Zijun; Jin, Xiaohang; Xu, Zhengguo

doi:10.1109/tim.2021.3054025

Cited by 70 publications

(18 citation statements)

References 33 publications

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“…Therefore, in time series data processing, the dependence of data at different time points is very important. 39…”

Section: Modified Empirical Mode Decomposition-multifractal Detrended...mentioning

confidence: 99%

“…Therefore, in time series data processing, the dependence of data at different time points is very important. 39 As depicted in Figure 1(a), 2N s ¼ 2½N =s� nonoverlapping sections of equivalent length s denoted as εðv,sÞðv ¼ 1; 2,…,2N s Þ are derived by segmenting the scale-dependence fluctuation ε s ðtÞ with a length of N in opposite order, respectively, where notation ½�� signifies the downward integral function. This segmentation method ignores the fact that the points on one local segment also have a close relationship with those on another local segment next to them.…”

Section: Empirical Mode Decomposition Algorithmmentioning

confidence: 99%

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Health condition monitoring of bearings based on multifractal spectrum feature with modified empirical mode decomposition-multifractal detrended fluctuation analysis

Chen

Yan

Meng

et al. 2022

Structural Health Monitoring

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Multifractal detrended fluctuation analysis (MFDFA) is proved to be a powerful tool for fault diagnosis of rotating machinery due to its ability to reveal multifractal structures hidden in nonstationary and nonlinear vibration signals. To overcome the discontinuity of the fitting scale-dependent trend and the poor adaptability of this algorithm, Empirical Mode Decomposition-Multifractal Detrended Fluctuation Analysis (EMD-MFDFA) is introduced. However, EMD-MFDFA runs into difficulties in reverse segmentation and the selection of the expected Intrinsic Mode Functions (IMFs). Aiming at solving these deficiencies, a Modified EMD-MFDFA (MEMD-MFDFA) approach with IMF selection strategy and Step-Moving Window (SMW) segmentation method is proposed in this paper. In MEMD-MFDFA, a metric for distinguishing deterministic and random components is established to select expected IMF components by scaling exponent. Meanwhile, SMW segmentation method is exploited to reduce the estimated errors caused by reverse segmentation. The robustness of the proposed method is investigated through comparing MEMD-MFDFA, MFDFA, and EMD-MFDFA by multifractality of simulated signals with different Signal-to-Noise Ratio (SNR). Furthermore, the proposed approach is applied to three bearing run-to-failure datasets containing three types of faults, and the results show that the multifeatures of the multifractal spectrum obtained by MEMD-MFDFA have the ability to simultaneously identify early fault and assess performance degradation of bearings.

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“…Therefore, in time series data processing, the dependence of data at different time points is very important. 39…”

Section: Modified Empirical Mode Decomposition-multifractal Detrended...mentioning

confidence: 99%

Section: Empirical Mode Decomposition Algorithmmentioning

confidence: 99%

Health condition monitoring of bearings based on multifractal spectrum feature with modified empirical mode decomposition-multifractal detrended fluctuation analysis

Chen

Yan

Meng

et al. 2022

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

“…LSTM can make full use of time correlation of data to process time series data. However, its complex internal structure leads to long training time, which is not conducive to practical application deployment [28]. GRU optimizes LSTM by combining forgetting gates and input gates to reduce the number of gates and improve the training efficiency of the network while ensuring memory ability.…”

Section: B Bidirectional Gated Recurrent Unitmentioning

confidence: 99%

Remaining Useful Life Interval Prediction for Complex System Based on BiGRU Optimized by Log-Norm

Yan

Liang

2022

IEEE Access

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The task of remaining useful life (RUL) uncertainty management is the major challenge in solving the failure of the complex mechanical system. Primary research methods use statistical models or stochastic processes to fit the distribution of historical degradation data. However, it is difficult to accurately capture the degradation information of monitoring big data through statistics in practice. In this paper, the prediction interval (PI) obtained by the proposed feature attention-log-norm bidirectional gated recurrent unit (FA-LBiGRU) model is adopted to quantify the prediction uncertainty of RUL. Initially, the critical feature vectors are extracted from multi-dimensional, nonlinear, and large-scale sensor signals using the feature attention mechanism. Additionally, the BiGRU network is used to model and learn the time-varying characteristics of the attention-weighted features from the forward and backward directions, and the network parameters are trained by the maximum log-likelihood loss function. Ultimately, the probability density function based on the lognormal distribution is calculated to measure the uncertainty of the equipment RUL. The effectiveness of the proposed method is verified through the well-known benchmark data set of the turbofan engines provided by NASA. The experimental results show that the proposed methods can obtain higher point prediction accuracy for the complex system compared with state-of-the-art approaches and highquality PIs satisfying real-time requirements.INDEX TERMS fusion model, gated recurrent unit, prediction intervals, remaining useful life, system prognostics, uncertainty management.

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“…The remaining useful life (RUL) prediction for rolling bearings is of great significant and practical value for the predictive maintenance of mechanical equipment and safe operation of industries [1,2]. In recent years, many scholars have studied and summarized RUL prediction methods [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction Method for Bearings Based on LSTM with Uncertainty Quantification

Yang

Peng

Xie

et al. 2022

Sensors

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To reduce the economic losses caused by bearing failures and prevent safety accidents, it is necessary to develop an effective method to predict the remaining useful life (RUL) of the rolling bearing. However, the degradation inside the bearing is difficult to monitor in real-time. Meanwhile, external uncertainties significantly impact bearing degradation. Therefore, this paper proposes a new bearing RUL prediction method based on long-short term memory (LSTM) with uncertainty quantification. First, a fusion metric related to runtime (or degradation) is proposed to reflect the latent degradation process. Then, an improved dropout method based on nonparametric kernel density is developed to improve estimation accuracy of RUL. The PHM2012 dataset is adopted to verify the proposed method, and comparison results illustrate that the proposed prediction model can accurately obtain the point estimation and probability distribution of the bearing RUL.

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Remaining Useful Life Prediction for Bearings Based on a Gated Recurrent Unit

Cited by 70 publications

References 33 publications

Health condition monitoring of bearings based on multifractal spectrum feature with modified empirical mode decomposition-multifractal detrended fluctuation analysis

Health condition monitoring of bearings based on multifractal spectrum feature with modified empirical mode decomposition-multifractal detrended fluctuation analysis

Remaining Useful Life Interval Prediction for Complex System Based on BiGRU Optimized by Log-Norm

Remaining Useful Life Prediction Method for Bearings Based on LSTM with Uncertainty Quantification

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