Degradation-trend-dependent Remaining Useful Life Prediction for Bearing with BiLSTM and Attention Mechanism

Chen, Yanan; Liu, Zhenxing; Zhang, Yong; Zheng, Xiujuan; Xie, Jin

doi:10.1109/ddcls52934.2021.9455600

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…In this section, unlike the existing RUL prediction techniques which predict the degradation index (DI) [41] or the root mean square(RMS) [42] from the vibration data, we use manifold learning to visualize the fitting curve between the prediction and the original data to evaluate the prediction performance of the model more intuitively. We use the vibration data of the first 1800 time points to predict the remaining vibration data on bearing 1_3 and bearing 2_3, respectively.…”

Section: Prediction Results Visualizationmentioning

confidence: 99%

“…In this section, our proposed HNCPM is compared with the state-of-the-art novel rolling bearing health-prediction methods based on CNN and BiLSTM models [41], and BiLSTM with attention mechanism [42]. In addition, we compare general encode and regression model combinations (i.e., SAE+GRU, CNN+LSTM) to evaluate the model prediction performance.…”

Section: Comparison With State-of-the Art Methodsmentioning

confidence: 99%

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

Qian

Chen³

et al. 2022

Lubricants

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In recent years, deep learning has become prevalent in Remaining Useful-Life (RUL) prediction of bearings. The current deep-learning-based RUL methods tend to extract high dimensional features from the original vibration data to construct the Health Indicators (HIs), and then use the HIs to predict the remaining life of the bearings. These approaches ignore the sequential relationship of the original vibration data and seriously affect the prediction accuracy. In order to tackle this problem, we propose a hard negative sample contrastive learning prediction model (HNCPM) with encoder module, GRU regression module and decoder module, used for feature embedding, regression RUL prediction and vibration data reconstruction, respectively. We introduce self-supervised contrast learning by constructing positive and negative samples of vibration data rather than constructing any health indicators. Furthermore, to avoid the subtle variability of vibration data in the health stage to aggravate the degradation features learning of the model, we propose the hard negative samples by cosine similarity, which are most similar to the positive sample. Meanwhile, a novel infoNCE and MSE-based loss function is derived and applied to the HNCPM to simultaneously optimize a lower bound on mutual information of the positive and negative sample over life cycle, as well as the discrepancy between true and predicted values of the vibration data, such that the model can learn the fine-grained degradation representations by predicting the future without any HIs as labels. The HNCPM is validated on the IEEE PHM Challenge 2012 dataset. The results demonstrate that the prediction performance of our model is superior to the state-of-the-art methods.

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Section: Prediction Results Visualizationmentioning

confidence: 99%

Section: Comparison With State-of-the Art Methodsmentioning

confidence: 99%

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

Qian

Chen³

et al. 2022

Lubricants

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“…Search space scanning and the exploration capability of POA in locating various search space regions are made possible by modeling the pelican's approach. The pelican's approach to the prey location can be defined by Equation (13):…”

Section: Moving Towards Prey (Exploration Phase)mentioning

confidence: 99%

“…However, LSTM can only make use of the correlation of logging data information in a single direction. Bi-directional long short-term memory (BiLSTM) can extract the features of the logging sequence from the front and back, respectively, along the depth, and make full use of the dependent information in the front and back sequences to predict the reservoir porosity [13]. Further, the attention mechanism is integrated into the hidden state by mapping weight value to strengthen the influence of important information [14].…”

Section: Introductionmentioning

confidence: 99%

Reservoir Porosity Prediction Based on BiLSTM-AM Optimized by Improved Pelican Optimization Algorithm

Qiao,

He,

Cui

et al. 2024

Energies

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To accurately predict reservoir porosity, a method based on bi-directional long short-term memory with attention mechanism (BiLSTM-AM) optimized by the improved pelican optimization algorithm (IPOA) is proposed. Firstly, the nonlinear inertia weight factor, Cauchy mutation, and sparrow warning mechanism are introduced to improve the pelican optimization algorithm (POA). Secondly, the superiority of IPOA is verified by using the CEC–2022 benchmark test functions. In addition, the Wilcoxon test is applied to evaluate the experimental results, which proves the superiority of IPOA against other popular algorithms. Finally, BiLSTM-AM is optimized by IPOA, and IPOA-BiLSTM-AM is used for porosity prediction in the Midlands basin. The results show that IPOA-BiLSTM-AM has the smallest prediction error for the verification set samples (RMSE and MAE were 0.5736 and 0.4313, respectively), which verifies its excellent performance.

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Attention mechanism in intelligent fault diagnosis of machinery: A review of technique and application

Chen

Zhang

et al. 2022

Measurement

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Degradation-trend-dependent Remaining Useful Life Prediction for Bearing with BiLSTM and Attention Mechanism

Cited by 4 publications

References 13 publications

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

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

Reservoir Porosity Prediction Based on BiLSTM-AM Optimized by Improved Pelican Optimization Algorithm

Attention mechanism in intelligent fault diagnosis of machinery: A review of technique and application

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