EMA remaining useful life prediction with weighted bagging GPR algorithm

Zhang, Yujie; Liu, Datong; Yu, Jinxiang; Yu, Peng; Peng, Xiyuan

doi:10.1016/j.microrel.2017.03.021

Cited by 40 publications

(9 citation statements)

References 40 publications

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“…Finally, we use the prediction results on these seven bearings to evaluate the performance of the proposed method. Root mean squared error (RMSE) and mean absolute percentage error (MAPE) are introduced to evaluate the prediction performance with the following formula 31 GPR, 32 SVM, 5 and extreme learning machine (ELM). 33 These four methods also use the same deep features extracted in section ''Deep feature extraction'' to train the regression model and then conduct RUL prediction for the bearing data in fast degradation state.…”

Section: Resultsmentioning

confidence: 99%

Predicting remaining useful life of rolling bearings based on deep feature representation and long short-term memory neural network

Mao

Tang

et al. 2018

Advances in Mechanical Engineering

104

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For bearing remaining useful life prediction problem, the traditional machine-learning-based methods are generally short of feature representation ability and incapable of adaptive feature extraction. Although deep-learning-based remaining useful life prediction methods proposed in recent years can effectively extract discriminative features for bearing fault, these methods tend to less consider temporal information of fault degradation process. To solve this problem, a new remaining useful life prediction approach based on deep feature representation and long short-term memory neural network is proposed in this article. First, a new criterion, named support vector data normalized correlation coefficient, is proposed to automatically divide the whole bearing life as normal state and fast degradation state. Second, deep features of bearing fault with good representation ability can be obtained from convolutional neural network by means of the marginal spectrum in Hilbert-Huang transform of raw vibration signals and health state label. Finally, by considering the temporal information of degradation process, these features are fed into a long short-term memory neural network to construct a remaining useful life prediction model. Experiments are conducted on bearing data sets of IEEE PHM Challenge 2012. The results show the significance of performance improvement of the proposed method in terms of predictive accuracy and numerical stability.

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Section: Resultsmentioning

confidence: 99%

Predicting remaining useful life of rolling bearings based on deep feature representation and long short-term memory neural network

Mao

Tang

et al. 2018

Advances in Mechanical Engineering

104

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“…For building an RUL prediction model, we choose LIB-SVM [41] as the implementation of SVM. We introduce the other three regression algorithms for comparison: linear regression (LR) [42], Gaussian process regression (GPR) [43] and least square SVM (LSSVM) [44]. For these four regression algorithms, model selection is conducted to choose the optimal hyper-parameters.…”

Section: B Experimental Settingmentioning

confidence: 99%

Prediction of Bearings Remaining Useful Life Across Working Conditions Based on Transfer Learning and Time Series Clustering

Mao¹,

Sun

et al. 2021

IEEE Access

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Recently, data-driven remaining useful life (RUL) prediction has become a promising tool in prognostics and health management for rolling bearings. In many actual applications, however, it is not easy to collect whole-life degradation data of bearings, while training with an insufficient amount of data would result in a biased RUL prediction model. If the monitoring data historically accumulated under different working conditions are introduced to facilitate model training, it probably tends to get inadequate prediction performance due to the violation of the precondition of independent and identical distribution (i.i.d.). To solve this problem, a new bearing RUL prediction approach is proposed by utilizing the transfer learning strategy. First, a new time series clustering algorithm is proposed to exploit clusters of different degradation series. By integrating phase space warping and dynamic time warping, this algorithm is able to measure the similarity by using global degradation information and then get better clustering performance. Second, a new temporal domain adaptation method is proposed to obtain the pivot feature set of different bearings based on meta-degradation information which is represented by the principal curve of each cluster. Finally, support vector machine is run to establish the prediction model by means of these features, and RUL of the target bearing can be predicted via the same feature adaptation. A theoretical analysis is also provided to prove that the proposed approach has an upper bound of information loss in the transfer process. Experimental results on the IEEE PHM challenge 2012 dataset demonstrate the effectiveness of the proposed approach.

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“…Note that the flatten layer is used as the deep feature representation of the vibration signals to be fed into the LSTM network for RUL prediction. The proposed method is compared with other methods, such as the SVM [45], linear regression (LR) [46], GPR [47], and extreme learning machine (ELM) [48], to show its superiority.…”

Section: Related Workmentioning

confidence: 99%

Time Series Multiple Channel Convolutional Neural Network with Attention-Based Long Short-Term Memory for Predicting Bearing Remaining Useful Life

Jiang

Lee

Zeng

2019

Sensors

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This paper proposes two deep learning methods for remaining useful life (RUL) prediction of bearings. The methods have the advantageous end-to-end property that they take raw data as input and generate the predicted RUL directly. They are TSMC-CNN, which stands for the time series multiple channel convolutional neural network, and TSMC-CNN-ALSTM, which stands for the TSMC-CNN integrated with the attention-based long short-term memory (ALSTM) network. The proposed methods divide a time series into multiple channels and take advantage of the convolutional neural network (CNN), the long short-term memory (LSTM) network, and the attention-based mechanism for boosting performance. The CNN performs well for extracting features from data with multiple channels; dividing a time series into multiple channels helps the CNN extract relationship among far-apart data points. The LSTM network is excellent for processing temporal data; the attention-based mechanism allows the LSTM network to focus on different features at different time steps for better prediction accuracy. PRONOSTIA bearing operation datasets are applied to the proposed methods for the purpose of performance evaluation and comparison. The comparison results show that the proposed methods outperform the others in terms of the mean absolute error (MAE) and the root mean squared error (RMSE) of RUL prediction.

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EMA remaining useful life prediction with weighted bagging GPR algorithm

Cited by 40 publications

References 40 publications

Predicting remaining useful life of rolling bearings based on deep feature representation and long short-term memory neural network

Predicting remaining useful life of rolling bearings based on deep feature representation and long short-term memory neural network

Prediction of Bearings Remaining Useful Life Across Working Conditions Based on Transfer Learning and Time Series Clustering

Time Series Multiple Channel Convolutional Neural Network with Attention-Based Long Short-Term Memory for Predicting Bearing Remaining Useful Life

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