Macroscopic–Microscopic Attention in LSTM Networks Based on Fusion Features for Gear Remaining Life Prediction

Qin, Yi; Xiang, Sheng; Chai, Yi; Chen, Haizhou

doi:10.1109/tie.2019.2959492

Cited by 151 publications

(39 citation statements)

References 32 publications

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“…In the essay of Wang et al [23][24][25], some methods were introduced and the validity of these methods was compared, including the quantiles method, empirical characteristic function method, logarithmic moment method, Monte Carlo method, etc. It was concluded that the CF accuracy method was better.…”

Section: Parameter Estimation With the Characteristic Functionmentioning

confidence: 99%

“…The small-data method is introduced in Section 2, The fLsm is introduced in Section 3, where we also analyze the model and LRD characteristics. The fLsm finite difference iterative forecasting model is proposed in Section 4, which establishes the finite-difference iterative forecasting model by making Langevin-type SDE [19] driven by fractional Levy stable motion, and the Langevin-type SDE [19] parameters estimated by the novel Characteristic Function (CF) method [23][24][25]. The wind speed forecasting results show the superiority of the method used in this paper (Section 5).…”

Section: Introductionmentioning

confidence: 96%

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Fractional Levy Stable and Maximum Lyapunov Exponent for Wind Speed Prediction

et al. 2020

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In this paper, a wind speed prediction method was proposed based on the maximum Lyapunov exponent (Le) and the fractional Levy stable motion (fLsm) iterative prediction model. First, the calculation of the maximum prediction steps was introduced based on the maximum Le. The maximum prediction steps could provide the prediction steps for subsequent prediction models. Secondly, the fLsm iterative prediction model was established by stochastic differential. Meanwhile, the parameters of the fLsm iterative prediction model were obtained by rescaled range analysis and novel characteristic function methods, thereby obtaining a wind speed prediction model. Finally, in order to reduce the error in the parameter estimation of the prediction model, we adopted the method of weighted wind speed data. The wind speed prediction model in this paper was compared with GA-BP neural network and the results of wind speed prediction proved the effectiveness of the method that is proposed in this paper. In particular, fLsm has long-range dependence (LRD) characteristics and identified LRD by estimating self-similarity index H and characteristic index α. Compared with fractional Brownian motion, fLsm can describe the LRD process more flexibly. However, the two parameters are not independent because the LRD condition relates them by αH > 1.

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Section: Parameter Estimation With the Characteristic Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Fractional Levy Stable and Maximum Lyapunov Exponent for Wind Speed Prediction

et al. 2020

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show abstract

“…RMS is a simple and practical HI, which is widely used in bearings residual life prediction. Hence, RMS is adopted as HI of bearings [26] in this paper.…”

Section: ) Construct a Health Indicator (Hi)mentioning

confidence: 99%

“…Huang et al [25] developed a bidirectional LSTM framework for RUL estimation. Qin et al [26] investigated an improved LSTM network for gear RUL estimation. Hu et al [27] proposed a reliable novel multistage attention networks multivariate for time series estimation.…”

Section: Introductionmentioning

confidence: 99%

Just Another Attention Network for Remaining Useful Life Prediction of Rolling Element Bearings

et al. 2020

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“…For example, Lu et al utilized the convolutional neural network (CNN) for in situ fault diagnosis [ 23 ]. Xiang and Qin applied long short term memory (LSTM) network for gear remaining life prediction [ 24 , 25 ]. The CNN can extract interrelations among input data utilizing convolutional filters, while LSTM can process time series information by characterizing long-term dependency.…”

Section: Introductionmentioning

confidence: 99%

Degradation Tendency Prediction for Pumped Storage Unit Based on Integrated Degradation Index Construction and Hybrid CNN-LSTM Model

Zhou

Shan

Liu

et al. 2020

Sensors

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Accurate degradation tendency prediction (DTP) is vital for the secure operation of a pumped storage unit (PSU). However, the existing techniques and methodologies for DTP still face challenges, such as a lack of appropriate degradation indicators, insufficient accuracy, and poor capability to track the data fluctuation. In this paper, a hybrid model is proposed for the degradation tendency prediction of a PSU, which combines the integrated degradation index (IDI) construction and convolutional neural network-long short-term memory (CNN-LSTM). Firstly, the health model of a PSU is constructed with Gaussian process regression (GPR) and the condition parameters of active power, working head, and guide vane opening. Subsequently, for comprehensively quantifying the degradation level of PSU, an IDI is developed using entropy weight (EW) theory. Finally, combining the local feature extraction of the CNN with the time series representation of LSTM, the CNN-LSTM model is constructed to realize DTP. To validate the effectiveness of the proposed model, the monitoring data collected from a PSU in China is taken as case studies. The root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) obtained by the proposed model are 1.1588, 0.8994, 0.0918, and 0.9713, which can meet the engineering application requirements. The experimental results show that the proposed model outperforms other comparison models.

show abstract

Macroscopic–Microscopic Attention in LSTM Networks Based on Fusion Features for Gear Remaining Life Prediction

Cited by 151 publications

References 32 publications

Fractional Levy Stable and Maximum Lyapunov Exponent for Wind Speed Prediction

Fractional Levy Stable and Maximum Lyapunov Exponent for Wind Speed Prediction

Just Another Attention Network for Remaining Useful Life Prediction of Rolling Element Bearings

Degradation Tendency Prediction for Pumped Storage Unit Based on Integrated Degradation Index Construction and Hybrid CNN-LSTM Model

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