Transferable convolutional neural network based remaining useful life prediction of bearing under multiple failure behaviors

Cheng, Huang; Kong, Xianwen; Chen, Gaige; Wang, Qibin; Wang, Rongbo

doi:10.1016/j.measurement.2020.108286

Cited by 137 publications

(46 citation statements)

References 43 publications

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“…This comparison proves the effectiveness of domain adaptation. Moreover, Cheng's method [51] can almost obtain the lowest prediction error besides the proposed approach, which proves the effectiveness of the domain adaptation based on L-STM model. Such comparative results indicate again that the transferring of temporal degradation information is beneficial for RUL prediction.…”

Section: E Results Of Rul Predictionmentioning

confidence: 63%

“…The second one by Mao et al The comparative results of all nine methods are shown in Table 5. Here the results of our approach and the four deep transfer learning-based methods (Zhu et al [21], Zhang et al [22],Sun et al [50] and Cheng et al [51]) are evaluated on the two transfer tasks shown in Table 1. For the other four methods, each of these five bearings is chosen in turn as the target bearing, while the other 6 bearings are used for training.…”

Section: E Results Of Rul Predictionmentioning

confidence: 99%

“…The seventh method by Sun et al[50] employs a stacked autoencoder (SAE) with Kullback-Leibler divergence plus transferring neuron weights to extract domain-invariant feature representation. The eighth method by Cheng et al[51] firstly utilizes CNN with MMD distance regularizer to extract common features, and then fine-tunes the LSTM parameters in target domain based on the source domain features. We think these works are on behalf of four kinds of typical methods: feature selection, deep learning, temporal data modeling and deep transfer learning, so they can provide a comprehensive comparison.…”

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confidence: 99%

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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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Section: E Results Of Rul Predictionmentioning

confidence: 63%

Section: E Results Of Rul Predictionmentioning

confidence: 99%

mentioning

confidence: 99%

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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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“…They showed that their performance provides superior performance compared to the other traditional approaches. Cheng et al [ 38 ] proposed a transferable convolutional neural network (TCNN) to learn domain invariant features for bearing RUL prediction. They showed that their model avoids the influence of kernel selection and present a better performance for RUL prediction.…”

Section: Background and Related Workmentioning

confidence: 99%

Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks

Kang

Çatal

Tekinerdoğan

2021

Sensors

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Predictive maintenance of production lines is important to early detect possible defects and thus identify and apply the required maintenance activities to avoid possible breakdowns. An important concern in predictive maintenance is the prediction of remaining useful life (RUL), which is an estimate of the number of remaining years that a component in a production line is estimated to be able to function in accordance with its intended purpose before warranting replacement. In this study, we propose a novel machine learning-based approach for automating the prediction of the failure of equipment in continuous production lines. The proposed model applies normalization and principle component analysis during the pre-processing stage, utilizes interpolation, uses grid search for parameter optimization, and is built with multilayer perceptron neural network (MLP) machine learning algorithm. We have evaluated the approach using a case study research to predict the RUL of engines on NASA turbo engine datasets. Experimental results demonstrate that the performance of our proposed model is effective in predicting the RUL of turbo engines and likewise substantially enhances predictive maintenance results.

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“…To minimize data, the distribution discrepancy of the feature space between sample and target domain, transferable convolution neural network (TCNN) and Generative adversarial networks (GAN) are proposed to predict the RUL 40,41 …”

Section: Related Workmentioning

confidence: 99%

Using temporal convolution network for remaining useful lifetime prediction

Chen

Liu

2020

Engineering Reports

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Because Convolutional Neural Network (CNN) can extract spatial feature, while Long Short‐Term Memory (LSTM) can learn temporal features, many methods combining CNN with LSTM are proposed for remaining useful lifetime prediction. In practice, it is better to learn temporal features from long history sequence data because of the slow inherently long‐term degradation process. However, LSTM is less efficient in processing long history sequence. To solve this problem, in this work, a temporal convolution network combining causal filters with dilated convolutions is used to expand the receptive field length of network. The network structure can be fixed through three key parameters, and the size of time window adopted for time sequence processing is the same as the receptive field length. These two characteristics allow the network to easily be applied for engineering purposes. The method is tested and evaluated using two well‐known datasets, namely the “Turbofan Engine Degradation Simulation Dataset C‐MAPSS” and “Milling Dataset.” The performance analysis shows that the proposed method outperforms more classical methods in terms of prediction accuracy.

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Transferable convolutional neural network based remaining useful life prediction of bearing under multiple failure behaviors

Cited by 137 publications

References 43 publications

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

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

Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks

Using temporal convolution network for remaining useful lifetime prediction

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