Degradation assessment of bearings with trend-reconstruct-based features selection and gated recurrent unit network

Xiao, Li; Liu, Zhenxing; Zhang, Yong; Zheng, Ying; Cheng, Cheng

doi:10.1016/j.measurement.2020.108064

Cited by 72 publications

(31 citation statements)

References 29 publications

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“…RUL prediction based on machine learning is attracting growing attention [43], [50]. Similar to fault diagnosis, representative methods of RULP include Support Vector Regression (SVR) [44], [45], HMM, GRP, CNN [46], Deep Belief Networks (DBN) [47], and Recurrent Neural Networks (RNN) [48], [49].…”

Section: B Machine Learning Based Methodsmentioning

confidence: 99%

State of AI-Based Monitoring in Smart Manufacturing and Introduction to Focused Section

Ding

Gao

Isaksson

et al. 2020

IEEE/ASME Trans. Mechatron.

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Section: B Machine Learning Based Methodsmentioning

confidence: 99%

State of AI-Based Monitoring in Smart Manufacturing and Introduction to Focused Section

Ding

Gao

Isaksson

et al. 2020

IEEE/ASME Trans. Mechatron.

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“…To further illustrate the performance of the proposed method, the RMSE and SMAPE are also utilized as performance evaluation metrics in the RUL prediction of XJTU-SY data sets. The prediction results of the proposed method are compared with the results reported in three published studies, including Huang's deep convolutional neural network-bootstrap integrated method [ 21 ], Hu's LSTM predictor trained simultaneously within a generative adversarial network (GAN) architecture [ 23 ], Ding's deep subdomain adaptive regression network [ 32 ], Li's deep adversarial neural networks-based method [ 33 ], and Xiao's trend-reconstruct-based features selection and gated recurrent unit network [ 47 ].…”

Section: Experimental Verificationmentioning

confidence: 99%

A Novel Method for Remaining Useful Life Prediction of Roller Bearings Involving the Discrepancy and Similarity of Degradation Trajectories

Luo

Lin

Liu

et al. 2021

Computational Intelligence and Neuroscience

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Accurate remaining useful life (RUL) prediction of bearings is the key to effective decision-making for predictive maintenance (PdM) of rotating machinery. However, the individual heterogeneity and different working conditions of bearings make the degradation trajectories of bearings different, resulting in the mismatch between the RUL prediction model established by the full-life training bearing and the testing bearings. To address this challenge, this paper proposes a novel RUL prediction method for roller bearings that considers the difference and similarity of degradation trajectories. In this method, a feature extraction method based on continuous wavelet transform (CWT) and convolutional autoencoder (CAE) is proposed to extract the deep features associated with bearing performance degradation before the degradation indicator (DI) is obtained by applying the self-organizing maps (SOM) method. Next, a dynamic time warping (DTW) based method is applied to perform the similarity matching of degradation trajectories of the training and testing bearings. Driven by the historical DIs of the given bearing, the grey forecasting model with full-order time power terms (FOTP-GM) is applied to model the degradation trajectory using a parameter optimization method. Then, the failure threshold of the given testing bearing can be determined using a data-driven method without manual intervention. Finally, the RUL of the given testing bearing can be estimated using the preset failure threshold and the optimized degradation trajectory model of the given testing bearing. The experimental results show that the proposed method retains the individual differences of bearing degradation trend, realizes the independent and reasonable bearing failure threshold setting, and improves the prediction accuracy of RUL.

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“…rough joint actions of such two layers, an operating state prediction model is built for cylinder gaskets according to the hybrid neural network based on PLSR and DNN. Additionally, basic working process of the hybrid neural network based on PLSR and DNN is shown in Figure 5 [55].…”

Section: Hybrid Neural Networkmentioning

confidence: 99%

The Optimization Research of Diesel Cylinder Gasket Parameters Based on Hybrid Neutral Network and Improved Grey Wolf Algorithm

Dong¹,

Liu²,

Liu³

et al. 2020

Mathematical Problems in Engineering

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In order to improve reliability and fatigue life of cylinder gaskets in heavy duty diesel engine, several methods and algorithms are applied to optimize operating factors of gaskets. Finite element method is utilized to figure out and analyze the temperature fields, thermal-mechanical coupling stress fields, and deformations of gasket. After determining the maximum values of three state parameters, the orthogonal experimental design method is adopted to analyze the influence rules of five operating factors on three state parameters of the gaskets and four factors which most significantly affect these state parameters are determined. Then, the method which uses operating factors to predict state parameters is established on the application of hybrid neuron network based on partial least squares regression and deep neural network. The comparison results between the predicted values and real values verified the accuracy of the hybrid neuron network method. Based on artificial bee colony algorithm, improvement is attached to the way three kinds of grey wolves locate preys in grey wolf algorithm and the way how using different hierarchy wolfs in grey wolf algorithm to determine three weight coefficients and the location of prey is put forward with. The method using artificial bee colony algorithm to optimize the grey wolf algorithm is called ABC and GWO. The proposed HNN and the ABC and GWO method are applied to work out operating factors values which correspond to optimal state parameters of gasket, and the gaskets are optimized according to the optimal values. It has been demonstrated by finite element analysis results that maximum temperature, maximum coupling stress, and the maximum deformation decrease to 6 K, 12.57 MPa, and 0.0925 mm compared to the original values, respectively, which proves the accuracy of the algorithm and the validity of the improvement.

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Degradation assessment of bearings with trend-reconstruct-based features selection and gated recurrent unit network

Cited by 72 publications

References 29 publications

State of AI-Based Monitoring in Smart Manufacturing and Introduction to Focused Section

State of AI-Based Monitoring in Smart Manufacturing and Introduction to Focused Section

A Novel Method for Remaining Useful Life Prediction of Roller Bearings Involving the Discrepancy and Similarity of Degradation Trajectories

The Optimization Research of Diesel Cylinder Gasket Parameters Based on Hybrid Neutral Network and Improved Grey Wolf Algorithm

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