Hard Negative Samples Contrastive Learning for Remaining Useful-Life Prediction of Bearings

Xu, Juan; Qian, Lei; Chen, Weiwei; Ding, Xu

doi:10.3390/lubricants10050102

Cited by 5 publications

(2 citation statements)

References 39 publications

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“…To verify the effectiveness of stage division in the proposed method, we, respectively, use full life cycle data and fast degradation data of the bearings to predict the RUL. We choose the root mean square error (RMSE) [26] as the metric to evaluate the model performance, expressed as follows:…”

Section: Comparison Results Using Full Life Cycle Data and Fast Degra...mentioning

confidence: 99%

AsdinNorm: A Single-Source Domain Generalization Method for the Remaining Useful Life Prediction of Bearings

Xu,

Ma,

Chen

et al. 2024

Lubricants

Self Cite

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The remaining useful life (RUL) of bearings is vital for the manipulation and maintenance of industrial machines. The existing domain adaptive methods have achieved major achievements in predicting RUL to tackle the problem of data distribution discrepancy between training and testing sets. However, they are powerless when the target bearing data are not available or unknown for model training. To address this issue, we propose a single-source domain generalization method for RUL prediction of unknown bearings, termed as the adaptive stage division and parallel reversible instance normalization model. First, we develop the instance normalization of the vibration data from bearings to increase data distribution diversity. Then, we propose an adaptive threshold-based degradation point identification method to divide the healthy and degradation stages of the run-to-failure vibration data. Next, the data from degradation stages are selected as training sets to facilitate the RUL prediction of the model. Finally, we combine instance normalization and instance denormalization of the bearing data into a unified GRU-based RUL prediction network for the purpose of leveraging the distribution bias in instance normalization and improving the generalization performance of the model. We use two public datasets to verify the proposed method. The experimental results demonstrate that, in the IEEE PHM Challenge 2012 dataset experiments, the prediction accuracy of our model with the average RMSE value is 1.44, which is 11% superior to that of the suboptimal comparison model (Transformer model). It proves that our model trained on one-bearing data achieves state-of-the-art performance in terms of prediction accuracy on multiple bearings.

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Section: Comparison Results Using Full Life Cycle Data and Fast Degra...mentioning

confidence: 99%

AsdinNorm: A Single-Source Domain Generalization Method for the Remaining Useful Life Prediction of Bearings

Xu,

Ma,

Chen

et al. 2024

Lubricants

Self Cite

View full text Add to dashboard Cite

show abstract

“…Zhang et al [ 34 ] proposed a self-attention-based perception and prediction framework based on Transformer, called DeepHealth. Xu et al [ 35 ] proposed a prediction model (HNCPM) that combines encoder, GRU regression module and decoder, through which the prediction of vibration data is realized. This model deploys an enhanced attention mechanism to capture global dependency from vibrational signals to forecast future signals and predict facility health.…”

Section: Introductionmentioning

confidence: 99%

Time Series Forecasting of Motor Bearing Vibration Based on Informer

Yang

Liu

et al. 2022

Sensors

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Electric energy, as an economical and clean energy, plays a significant role in the development of science and technology and the economy. The motor is the core equipment of the power station; therefore, monitoring the motor vibration and predicting time series of the bearing vibration can effectively avoid hazards such as bearing heating and reduce energy consumption. Time series forecasting methods of motor bearing vibration based on sliding window forecasting, such as CNN, LSTM, etc., have the problem of error accumulation, and the longer the time-series forecasting, the larger the error. In order to solve the problem of error accumulation caused by the conventional methods of time series forecasting of motor bearing vibration, this paper innovatively introduces Informer into time series forecasting of motor bearing vibration. Based on Transformer, Informer introduces ProbSparse self-attention and self-attention distilling, and applies random search to optimize the model parameters to reduce the error accumulation in forecasting, achieve the optimization of time and space complexity and improve the model forecasting. Comparing the forecasting results of Informer and those of other forecasting models in three publicly available datasets, it is verified that Informer has excellent performance in time series forecasting of motor bearing vibration and the forecasting results reach 10−2∼10−6.

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Enhancing prognostics for sparse labeled data using advanced contrastive self-supervised learning with downstream integration

Deng,

Nguyen,

Gogu

et al. 2024

Engineering Applications of Artificial Intelligence

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Hard Negative Samples Contrastive Learning for Remaining Useful-Life Prediction of Bearings

Cited by 5 publications

References 39 publications

AsdinNorm: A Single-Source Domain Generalization Method for the Remaining Useful Life Prediction of Bearings

AsdinNorm: A Single-Source Domain Generalization Method for the Remaining Useful Life Prediction of Bearings

Time Series Forecasting of Motor Bearing Vibration Based on Informer

Enhancing prognostics for sparse labeled data using advanced contrastive self-supervised learning with downstream integration

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