The real-time big data processing method based on LSTM or GRU for the smart job shop production process

Wang, Chuang; Du, Wenbo; Zhu, Zhen; Yue, Zhifeng

doi:10.1177/1748302620962390

Cited by 12 publications

(6 citation statements)

References 23 publications

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“…The LSTM network also has some variants; one is the gated recurrent unit (GRU) network [44], which needs fewer calculations than an LSTM cell. As a result, the GRU networks usually show a higher training efficiency than the LSTM networks [45,46].…”

Section: Introductionmentioning

confidence: 99%

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

Wang

Liu

Yue

et al. 2020

Water

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A gated recurrent unit (GRU) network, which is a kind of artificial neural network (ANN), has been increasingly applied to runoff forecasting. However, knowledge about the impact of different input data filtering strategies and the implications of different architectures on the GRU runoff forecasting model’s performance is still insufficient. This study has selected the daily rainfall and runoff data from 2007 to 2014 in the Wei River basin in Shaanxi, China, and assessed six different scenarios to explore the patterns of that impact. In the scenarios, four manually-selected rainfall or runoff data combinations and principal component analysis (PCA) denoised input have been considered along with single directional and bi-directional GRU network architectures. The performance has been evaluated from the aspect of robustness to 48 various hypermeter combinations, also, optimized accuracy in one-day-ahead (T + 1) and two-day-ahead (T + 2) forecasting for the overall forecasting process and the flood peak forecasts. The results suggest that the rainfall data can enhance the robustness of the model, especially in T + 2 forecasting. Additionally, it slightly introduces noise and affects the optimized prediction accuracy in T + 1 forecasting, but significantly improves the accuracy in T + 2 forecasting. Though with relevance (R = 0.409~0.763, Grey correlation grade >0.99), the runoff data at the adjacent tributary has an adverse effect on the robustness, but can enhance the accuracy of the flood peak forecasts with a short lead time. The models with PCA denoised input has an equivalent, even better performance on the robustness and accuracy compared with the models with the well manually filtered data; though slightly reduces the time-step robustness, the bi-directional architecture can enhance the prediction accuracy. All the scenarios provide acceptable forecasting results (NSE of 0.927~0.951 for T + 1 forecasting and 0.745~0.836 for T + 2 forecasting) when the hyperparameters have already been optimized. Based on the results, recommendations have been provided for the construction of the GRU runoff forecasting model.

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Section: Introductionmentioning

confidence: 99%

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

Wang

Liu

Yue

et al. 2020

Water

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“…Another work in [5] also supported the results of [6] stating that GRU based networks perform faster with near or better performance when compared to LSTM networks. LSTM and GRU being two successful architectures, some papers focus on the comparison of those two techniques as in [5], [8], [14]. It has been mainly reported that the learning speed of the GRU based models has exceeded the learning speed of LSTM based models and that they both performed well with high efficiency in various PdM problems.…”

Section: Related Workmentioning

confidence: 99%

“…Among the AI techniques, the time sequence analysis based techniques such as Recurrent Neural Networks (RNN) [2], [3] and Long Short Term Memory (LSTM) [4] networks have been widely used. Recent interest mainly evolved around LSTM and Gated Recurrent Unit (GRU) [5]- [8].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Prediction of Remaining-Useful-Life and Failure-Likelihood with GRU-based Deep Networks for Predictive Maintenance Analysis

Kaleli

Unal

Özer

2021

2021 44th International Conference on Telecommunications and Signal Processing (TSP)

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Predictive maintenance (PdM) has been an integral part of large industrial sites collecting data from multiple sensors to reduce the maintenance power and costs with the advent of Industry 4.0. Two of the major problems in PdM used at large industrial sites are: (i) the prediction of remaining useful life (RUL); (ii) the prediction of the likelihood of failing within a predefined time period. While data oriented maintenance predictions were heavily focused on using classical techniques for such problems, recent interest shifted towards utilizing AI based solutions due to the better generalization capabilities of deep solutions. Among the time-sequence based deep networks, RNN, GRU and LSTM based networks are the most frequently used solutions. GRUs have demonstrated their faster learning capabilities with near or better prediction performance on certain tasks already. However, predicting multiple PdM tasks including both RUL and failure detection, simultaneously within the same network in an end to end manner with GRUs has not been much studied in the literature before. In this paper, we introduce a solution to predict those two tasks simultaneously within the same network based on GRUs. In our experiments we compare the performance of GRU layers to LSTM and RNN layers and report their performance on NASA dataset.

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“…LSTM and gated recurrent units overcome the longterm dependence of RNNs on the inability to capture data by introducing a gating mechanism. 21,23 Zhang Y et al 9 predicted the battery RUL by learning the long-term correlation of capacity degradation of battery using LSTM. Meanwhile, to improve the accuracy and effectiveness of the estimation models, many works have been done to improve the existing estimation models.…”

Section: Introductionmentioning

confidence: 99%

Attention-based CNN-BiLSTM for SOH and RUL estimation of lithium-ion batteries

Zhu

Yang

Liu

et al. 2022

Journal of Algorithms & Computational Technology

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The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries describe the current aging degree of the batteries from different perspectives, and accurate and efficient battery health estimation is essential for their safe use. To improve the effectiveness and accuracy of the batteries’ health assessment models, this paper proposes a new method for SOH and RUL estimation of lithium-ion batteries. Convolutional neural networks (CNNs), bi-directional long short-term memory (BiLSTM), and attention mechanism (AM) to build a hybrid network model for capacity estimation of lithium-ion batteries, and further calculate the SOH and RUL estimation results. By using Center for Advanced Life Cycle Engineering (CALCE) lithium-ion battery capacity degradation data, we extracted the battery health indicator (HI) and verified the reasonableness of HI selection by using Gray Relational Analysis (GRA) and compared it with other network models to calculate the prediction accuracy by various evaluation indexes. The experimental results show that the method has higher estimation accuracy while avoiding the construction of complex battery mechanism degradation models and is highly generalized.

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The real-time big data processing method based on LSTM or GRU for the smart job shop production process

Cited by 12 publications

References 23 publications

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

Simultaneous Prediction of Remaining-Useful-Life and Failure-Likelihood with GRU-based Deep Networks for Predictive Maintenance Analysis

Attention-based CNN-BiLSTM for SOH and RUL estimation of lithium-ion batteries

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