An end-to-end framework for remaining useful life prediction of rolling bearing based on feature pre-extraction mechanism and deep adaptive transformer model

Su, Xuanyuan; Liu, Hongmei; Tao, Laifa; Lu, Chen; Suo, Mingliang

doi:10.1016/j.cie.2021.107531

Cited by 71 publications

(21 citation statements)

References 44 publications

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“…To solve this problem, Hochreiter and Schmidhuber proposed a new network structure called LSTM (Hochreiter and Schmidhuber, 1997). Based on RNN, LSTM adds an information memory pathway and performs information update with the computation of three gates (forgetting gate, input gate and output gate) (Su et al , 2021). This novel structure effectively captures long-term dependencies by additionally introducing a flow of memory information.…”

Section: Methodsmentioning

confidence: 99%

“…FCLNN and RNCAM do not require feature extraction and directly use the raw vibration signal of the bearing as the input, and the size of the input data is 2,560 × 1. The third model is the attention transformer (AT) proposed by (Su et al , 2021). For the first time, AT uses LSTM for adaptive location coding and uses the self-attentive mechanism for feature extraction.…”

Section: Experimental Verificationmentioning

confidence: 99%

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Remaining useful life prediction of rolling bearings based on convolutional recurrent attention network

Zhang

Shao

et al. 2022

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Purpose The current studies on remaining useful life (RUL) prediction mainly rely on convolutional neural networks (CNNs) and long short-term memories (LSTMs) and do not take full advantage of the attention mechanism, resulting in lack of prediction accuracy. To further improve the performance of the above models, this study aims to propose a novel end-to-end RUL prediction framework, called convolutional recurrent attention network (CRAN) to achieve high accuracy. Design/methodology/approach The proposed CRAN is a CNN-LSTM-based model that effectively combines the powerful feature extraction ability of CNN and sequential processing capability of LSTM. The channel attention mechanism, spatial attention mechanism and LSTM attention mechanism are incorporated in CRAN, assigning different attention coefficients to CNN and LSTM. First, features of the bearing vibration data are extracted from both time and frequency domain. Next, the training and testing set are constructed. Then, the CRAN is trained offline using the training set. Finally, online RUL estimation is performed by applying data from the testing set to the trained CRAN. Findings CNN-LSTM-based models have higher RUL prediction accuracy than CNN-based and LSTM-based models. Using a combination of max pooling and average pooling can reduce the loss of feature information, and in addition, the structure of the serial attention mechanism is superior to the parallel attention structure. Comparing the proposed CRAN with six different state-of-the-art methods, for the predicted results of two testing bearings, the proposed CRAN has an average reduction in the root mean square error of 57.07/80.25%, an average reduction in the mean absolute error of 62.27/85.87% and an average improvement in score of 12.65/6.57%. Originality/value This article provides a novel end-to-end rolling bearing RUL prediction framework, which can provide a reference for the formulation of bearing maintenance programs in the industry.

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Section: Methodsmentioning

confidence: 99%

Section: Experimental Verificationmentioning

confidence: 99%

Remaining useful life prediction of rolling bearings based on convolutional recurrent attention network

Zhang

Shao

et al. 2022

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“…Ma et al [53] created a variation of the G-Transformer model architecture that uses the encoder from traditional Transformer models as it is applied to natural language processing for sampling and extracting features for PM. The Adaptive Transformer (AT) is a modified deep attention architecture that handles temporal data of the low-level features related to RUL to minimize the recurring problem of vanishing gradients in prediction [117].…”

Section: ) Transformermentioning

confidence: 99%

“…[26,27,38,55,58,59,65,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128] …”

mentioning

confidence: 99%

Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

Ramezani¹,

Cummins²,

Killen³

et al. 2023

Preprint

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<ul> <li>This work summarizes the state-of-the-art data-driven methods for prediction of the Remaining Useful Life<br> (RUL)<br> </li> <li>It discusses challenges and open problems faced in PdM<br> </li> <li>This study presents a discussion on the new problems that need to be considered towards the Industry 4.0 goals<br> </li> <li>We propose the future direction for each challenge discussed in this article</li> </ul>

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Section: ) Transformermentioning

confidence: 99%

Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

et al. 2023

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Early detection of faulty patterns and timely scheduling of maintenance events can minimize risk to the underlying processes and increase a system's lifespan, reliability, and availability. Two main data-driven approaches are used in the literature to determine the Remaining Useful Life (RUL): direct calculation from raw data and indirect analysis by revealing the transitions from one latent state to another and highlighting degradation in a system's Health Indices. The present study discusses the state-of-theart data-driven methods introduced for RUL prediction in predictive maintenance, by looking at their capabilities, scalability, performance, and weaknesses. We will also discuss the challenges faced with the current approaches and the future directions to tackle the current limitations.

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An end-to-end framework for remaining useful life prediction of rolling bearing based on feature pre-extraction mechanism and deep adaptive transformer model

Cited by 71 publications

References 44 publications

Remaining useful life prediction of rolling bearings based on convolutional recurrent attention network

Remaining useful life prediction of rolling bearings based on convolutional recurrent attention network

Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

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