2023
DOI: 10.1002/qre.3314
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A CNN‐BiLSTM‐Bootstrap integrated method for remaining useful life prediction of rolling bearings

Abstract: Rolling bearings, an essential fundamental component in machinery and equipment, have been widely used. Predicting the remaining useful life (RUL) of rolling bearings helps maintain the reliability of mechanical systems. Accurate prediction of RUL requires extracting deep features in complex non-linear vibration signals, the prediction results often vary widely. This paper proposes a RUL prediction method based on convolutional neural network (CNN), bi-directional long-short term memory (BiLSTM), and bootstrap… Show more

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Cited by 26 publications
(5 citation statements)
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“…The bootstrap technique refers to a statistical approach that is utilized for the estimation of uncertainty [34]. Meanwhile, resampling from the raw signal is the essential step of the bootstrap technique.…”
Section: Bootstrap Methodsmentioning
confidence: 99%
“…The bootstrap technique refers to a statistical approach that is utilized for the estimation of uncertainty [34]. Meanwhile, resampling from the raw signal is the essential step of the bootstrap technique.…”
Section: Bootstrap Methodsmentioning
confidence: 99%
“…Traditional methods typically rely on extensive data to construct a model while our approach requires only the initial 10% of the battery dataset to effectively build the predictive model. The comparative experiments will be conducted using the LSTM model, Transformer, Bi-directional LSTM 42 and Attention-LSTM to make the prediction on the battery capacity in the same prediction length. The hyperparameters of these models can be seen from Table 7.…”
Section: The Comparison Of Different Prediction Methodsmentioning
confidence: 99%
“…These tools, governed by computer programming, efficiently perform complex and precise machining tasks. They are critically important in industries like aerospace, automotive manufacturing, mold production, and heavy industry [81][82][83][84]. The primary attributes of these tools include robust machining capabilities and remarkable stability.…”
Section: Illustrative Examplementioning
confidence: 99%