Remaining Useful Life Prediction of Rolling Bearings Using GRU-DeepAR with Adaptive Failure Threshold

Li, Jiahui; Wang, Zhihai; Liu, Xiaoqin; Feng, Zhengjiang

doi:10.3390/s23031144

Cited by 12 publications

(6 citation statements)

References 12 publications

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“…And the DeepAR model is used to predict the slope displacement by Dong [27], which the prediction accuracy of DeepAR model was verified by mean absolute error, root mean square error and goodness of fit. Li et al [28] proposed a method for predicting the residual service life of rolling bearing based on gru-depth autoregressive model with self-adaptive failure threshold [29].…”

Section: Deepar Model Theorymentioning

confidence: 99%

Analysis and Forecast of Railway Freight Volume based on Prophet-Deep AR Model

2023

Teh. vjesn.

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The research on railway freight volume forecast is of great significance to the allocation of railway freight transport resources, the formulation of transport plans and the organization of railway freight transport. This study, by fully mining the railway freight ticket data information, put forward the precise forecast model of railway freight volume under different types of freight. Firstly, the railway freight ticket data are cleaned, regulated and integrated, and the time series of the daily number of railway freight trains are constructed, get the trend, periodicity and holiday data of railway traffic data, and set the parameters of Chinese holidays and rest days according to the demand characteristics of different categories. Secondly, the forecasting result of the Prophet is taken as a cooperative parameter. DeepAR is used to forecast, and a combined model of the Prophet-DeepAR is constructed. Finally, the combined model was validated with Shanghai Railway Bureau data from January 1, 2015 to December 31, 2018 for the food and tobacco category, and with Prophet-DeepAR, LSTM, Wavelet, BILSTM, and Prophet-LSTM, prophet-wavelet and Prophet-Bilstm are used to compare the prediction results. The results show that the Prophet-DeepAR model can extract the multi-dimensional periodicity of freight traffic and mine the trend information of freight traffic, and get the prediction result with high precision. It has better accuracy than other models.

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Section: Deepar Model Theorymentioning

confidence: 99%

Analysis and Forecast of Railway Freight Volume based on Prophet-Deep AR Model

2023

Teh. vjesn.

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“…It is important to note that predicting the RUL directly after the initial degradation stage can save computational costs, improve algorithm efficiency, and enhance model accuracy throughout the entire life evolution of rolling bearings. 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.…”

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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“…Yan et al employed a support vector machine (SVM) to distinguish various stages of bearing degradation, which to some extent differentiated normal from faulty stages, but failed to identify the early micro-faults [7]. Li et al extracted traditional health features from the vibration signals of rolling bearings to ascertain the points of deterioration, relying on the trajectory of the cumulative maximum of root mean square (RMS) [8]. Cheng et al developed an adaptive kernel spectral clustering model, which adeptly identified the incipient degradation points of rolling bearings through the adaptive discernment of multidimensional deterioration features amalgamated from the time domain, frequency domain, and time-frequency domain [9].…”

Section: Introductionmentioning

confidence: 99%

Similarity indicator and CG-CGAN prediction model for remaining useful life of rolling bearings

Yang,

Binbin,

Cancan

et al. 2024

Meas. Sci. Technol.

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To tackle the challenges of performing early fault warning and improving the prediction accuracy for the remaining useful life (RUL) of rolling bearings, this paper proposes a similarity health indicator and a predictive model of CG-CGAN, which relies on a Conditional Generative Adversarial Network (CGAN) that combines one-dimensional Convolutional Neural Network (CNN) with a Bidirectional Gate Recurrent Unit (Bi-GRU). This framework provides a comprehensive theoretical foundation for RUL prediction of rolling bearings. The similarity health indicator allows for early fault warning of rolling bearings without expert knowledge. Within the CGAN framework, the inclusion of constraints guides the generation of samples in a more targeted manner. Additionally, the proposed CG-CGAN model incorporates Bi-GRU to consider both forward and backward information, thus improving the precision of RUL forecasting. Firstly, the similarity indicator between the vibration signals of the rolling bearing over its full life span and the standard vibration signals (healthy status) is calculated. This indicator helps to determine the early deterioration points of the rolling bearings. Secondly, the feature matrix composed of traditional health indicators and similarity health indicator, is utilized to train and test the proposed CG-CGAN model for RUL prediction. Finally, to corroborate the efficacy of the proposed method, two sets of real experiment data of rolling bearing accelerated life from the Intelligent Maintenance Systems (IMS) are utilized. Experimental findings substantiate that the proposed similarity health indicator offers precocious fault alerts and precisely delineates the performance diminution of the rolling bearing. Furthermore, the put-forward CG-CGAN model achieves high-precision RUL prediction of rolling bearing.

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Remaining Useful Life Prediction of Rolling Bearings Using GRU-DeepAR with Adaptive Failure Threshold

Cited by 12 publications

References 12 publications

Analysis and Forecast of Railway Freight Volume based on Prophet-Deep AR Model

Analysis and Forecast of Railway Freight Volume based on Prophet-Deep AR Model

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

Similarity indicator and CG-CGAN prediction model for remaining useful life of rolling bearings

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