LSTM networks based on attention ordered neurons for gear remaining life prediction

Xiang, Sheng; Qin, Yi; Zhu, Caichao; Wang, Yangyang; Chen, Haizhou

doi:10.1016/j.isatra.2020.06.023

Cited by 76 publications

(30 citation statements)

References 28 publications

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“…Xiang et al [187] constructed HI based on the characteristics of gear. Based on LSTM, they proposed a method for predicting the wear life of the gear.…”

Section: Dtmentioning

confidence: 99%

Digital Twin-Driven Remaining Useful Life Prediction for Gear Performance Degradation: A Review

Liu

Dong

2021

Journal of Computing and Information Science in Engineering

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As a transmission component, the gear has been obtained widespread attention. The remaining useful life (RUL) prediction of gear is critical to the prognostics health management (PHM) of gear transmission systems. The digital twin (DT) provides support for gear RUL prediction with the advantages of rich health information data and accurate health indicators (HI). This paper reviews digital twin-driven RUL prediction methods for gear performance degradation, from the view of digital twin-driven physical model-based and virtual model-based prediction method. From the view of physical model-based one, it includes prediction model based on gear crack, gear fatigue, gear surface scratch, gear tooth breakage, and gear permanent deformation. From the view of digital twin-driven virtual model-based one, it includes non-deep learning methods, and deep learning methods. Non-deep learning methods include wiener process, gamma process, hidden Markov model (HMM), regression-based model, proportional hazard model. Deep learning methods include deep neural networks (DNN), deep belief networks (DBN), convolutional neural networks (CNN), and recurrent neural networks (RNN), etc. It mainly summarizes the performance degradation and life test of various models in gear, and evaluates the advantages and disadvantages of various methods. In addition, it encourages future works.

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“…Xiang et al [187] constructed HI based on the characteristics of gear. Based on LSTM, they proposed a method for predicting the wear life of the gear.…”

Section: Dtmentioning

confidence: 99%

Digital Twin-Driven Remaining Useful Life Prediction for Gear Performance Degradation: A Review

Liu

Dong

2021

Journal of Computing and Information Science in Engineering

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“…[9]. Xiang et al [10] proposed a novel LSTM framework. Attention-guided ordered neurons are applied in this framework to achieve the accurate gear remaining useful life prediction.…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction of Rolling Bearings Based on Multiscale Convolutional Neural Network with Integrated Dilated Convolution Blocks

Wang

Shi

Xiong

et al. 2021

Shock and Vibration

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Remaining useful life (RUL) prediction is necessary for guaranteeing machinery’s safe operation. Among deep learning architectures, convolutional neural network (CNN) has shown achievements in RUL prediction because of its strong ability in representation learning. Features from different receptive fields extracted by different sizes of convolution kernels can provide complete information for prognosis. The single size convolution kernel in traditional CNN is difficult to learn comprehensive information from complex signals. Besides, the ability to learn local and global features synchronously is limited to conventional CNN. Thus, a multiscale convolutional neural network (MS-CNN) is introduced to overcome these aforementioned problems. Convolution filters with different dilation rates are integrated to form a dilated convolution block, which can learn features in different receptive fields. Then, several stacked integrated dilated convolution blocks in different depths are concatenated to extract local and global features. The effectiveness of the proposed method is verified by a bearing dataset prepared from the PRONOSTIA platform. The results turn out that the proposed MS-CNN has higher prediction accuracy than many other deep learning-based RUL methods.

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“…Sheng Xiang et al [31] proposed a novel long-and short-term memory neural network with weight amplification for accurate prediction of the remaining gear life in order to ensure the healthy operating conditions of gears. Xiang et al [32] proposed LSTM networks based on attention ordered neurons for gear remaining life prediction, and the experiments show the superiority of the new gear RUL prediction methodology based on LSTM-AON compared to the current prediction methods. Although these methods are widely used in the field of prediction, there are still some deficiencies.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Prediction Method of Rolling Bearing Performance Degradation Based on Attention-LSTM

Wang

Yang

et al. 2021

Shock and Vibration

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It is significant for the evaluation and prediction of the performance degradation of rolling bearings. However, the degradation stage division of the rolling bearing performance is not obvious in traditional methods, and the prediction accuracy is low. Therefore, an Attention-LSTM method is proposed to improve the evaluation and prediction of the performance degradation of rolling bearings. First, to reduce the uncertainty of the manual intervention, performance degradation characteristic indexes of rolling bearings are evaluated and screened by the correlation, the monotonicity, and the robustness. Second, the original characteristic indicator curve is divided into the Health Indicator (HI) curve and the residual curve by means of fixed-window averaging to quantitatively and intuitively reflect the deterioration degree of the rolling bearing performance. Finally, the Attention mechanism is combined with the LSTM model, and a scoring function is established to enhance the prediction accuracy. The scoring function is used to adjust the intermediate output state weight of the LSTM model and improve the prediction accuracy. The appropriate network structure and the parameter configuration are determined, and the prediction model of rolling bearing degradation performance is established. Compared with other models, the method proposed by this paper makes full use of the historical data and is more sensitive to the key information in the long time series, and the eRMSE index and the eMAE index of the two sets of experimental data are minimum, and the prediction accuracy of rolling bearing degradation performance is higher. The model has the strong robustness and the generalization ability, which has the important engineering practical value for the prediction of the equipment health state.

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LSTM networks based on attention ordered neurons for gear remaining life prediction

Cited by 76 publications

References 28 publications

Digital Twin-Driven Remaining Useful Life Prediction for Gear Performance Degradation: A Review

Digital Twin-Driven Remaining Useful Life Prediction for Gear Performance Degradation: A Review

Remaining Useful Life Prediction of Rolling Bearings Based on Multiscale Convolutional Neural Network with Integrated Dilated Convolution Blocks

Evaluation and Prediction Method of Rolling Bearing Performance Degradation Based on Attention-LSTM

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