Improved GRU prediction of paper pulp press variables using different pre-processing methods

Figures & data

Table 1. Summary of the keywords searched and total articles found in different search platforms.

	# Scopus	# WOS	# ScienceDirect
Keywords		”Predictive Maintenance”
Total of documents	2,587	3,308	2,730
Keywords		”Predictive Maintenance”
		”Recurrent Neural Network”
Total of documents	66	94	337
Keywords		”Predictive Maintenance”,
		”Recurrent Neural Network”,”GRU”
Total of documents	14	17	90
Keywords		”Predictive Maintenance”,
		”Recurrent Neural Network”,”GRU”,
		”Pre-Processing Methods”
Total of documents	0	0	3
Keywords		”Predictive Maintenance”,
		”Recurrent Neural Network”,”GRU”,
		”Pre-Processing Methods”, ”LOWESS”
Total of documents	0	0	0

Table 2. Comparative table showing the methods and results of the most relevant papers found.

Author	Focus	Concept Theoretical	Method	Sample	Findings
		Model
Dai et al. (2022)	Impact of data fluctuations on forecast accuracy	Data processing GRU and Random Forest	LOWESS Smoothing	Photovoltaic power generation	LOWESS smoothing can generate the smallest prediction error. Optimize the prediction performance of GRU model.
He et al. (2022)	Voltage Prediction	Auto-encoder based health indicator and LSTM network	LOWESS Smoothing	Voltage	GRU model is most suitable for the prediction of photovoltaic power generation. The method is more suitable for the hort-term forecasting than the medium and long-term forecasting. Good prediction between different load profiles.
F.-K. Wang et al. (2022)	Online useful life batteries prediction	Bi-LSTM	LOWESS Smoothing	Capacity(Ah)	The proposed online RUL prediction method proves to achieve better prediction results than LSTM, LSTM-AT, and Bi-LSTM models.

Figure 1. The cell structure of a Gated Recurrent Unit. (Mateus et al., 2021).

Figure 2. Diagram showing the flow the process data, from the press’ sensors to predictions.

Figure 3. Plot of the variables for press number 4, before any data pre-processing. The variables contain a large amount of noise.

Figure 4. Plot of the variables for press number 4, after data pre-processing. The variables contain a low amount of noise.

Figure 5. Variable autocorrelations, before cleaning and filtering the data.

Figure 6. Autocorrelation for the all variables, obtained after cleaning and smoothing the data using LOWESS with 36 days window.

Figure 7. RMSE of the best models for press 2, using the two different methods for pre-processing data, for the smaller and larger GRU networks. Method 1 only removes discrepant data. Method 2 smoothes the data using a LOWESS filter. Method (1,2) is the application of both. (a) prediction test with 50 GRU units with the two data processing methods, (b) prediction test with 500 GRU units with the two data processing methods.

Figure 8. RMSE for predictions of press 4 using the different data pre-processing methods. LOWESS filtering and 500 GRU units result in smaller RMSE errors. (a) prediction test with 50 GRU units with the two data processing methods, (b) prediction test with 500 GRU units with the two data processing methods.

Figure 9. Signals and forecast results for press 2, with 30 day advance, using the two data processing methods, both removal of discrepant data and data smoothing using LOWESS filtering with 36 days window. The blue lines represent the actual value. The Orange and green lines are predictions, respectively, in the train and test subsets.

Table 3. Prediction error results for 30 days advance forecast, using the two data preprocessing methods, removal of discrepant data and smoothing (LOWESS 36 days), for the 500 unit GRU with 5 days window, for press 2.

			Prediction errors for press 2
	C. Intensity	Hydraulic	Torque	VAT	Velocity	Temperature
MAPE	0.62	1.85	2.24	3.91	10.27	0.96
MAE	0.2	1.39	0.35	0.82	0.57	0.38
RMSE	0.23	1.55	0.37	0.95	0.6	0.5

Figure 10. Signals and forecast results for press 4, with 30 day advance, using the two data processing methods, both removal of discrepant data and data smoothing using LOWESS filtering with 36 days window. The blue lines represent the actual value. The Orange and green lines are predictions, respectively, in the train and test subsets.

Table 4. Prediction error results for 30 days advance forecast, using the two data preprocessing methods, removal of discrepant data and smoothing (LOWESS 36 days), for the 500 unit GRU with 5 days window, for press 4.

			Prediction errors for press 4
	C. Intensity	Hydraulic	Torque	VAT	Velocity	Temperature
MAPE	1.2	1.12	2.32	1.6	2.77	1.36
MAE	0.27	0.8	0.18	0.51	0.26	0.5
RMSE	0.30	1.00	0.20	0.61	0.30	0.69

Table

AI	Artificial Intelligence
ANN	Artificial Neural Networks
IoT	Internet of Things
IQR	Interquartile Range
NN	Neural Networks
GRU	Gated Recurrent Units
LSTM	Long Short-Term Memory
MAE	Mean Absolute Error
MAPE	Mean Absolute Percentage Error
RMSE	Root Mean Square Error
RNN	Recurrent Neural Networks
AF	Activation Function

Dai, Y., Wang, Y., Leng, M., Yang, X., & Zhou, Q. (2022). Lowess smoothing and random forest based gru model: A short-term photovoltaic power generation forecasting method. Energy, 256, 124661. https://doi.org/10.1016/j.energy.2022.124661

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He, K., Liu, Z., Sun, Y., Mao, L., & Lu, S. (2022). Degradation prediction of proton exchange membrane fuel cell using auto-encoder based health indicator and long short-term memory network. International Journal of Hydrogen Energy, 47, 35055–35067. https://doi.org/10.1016/j.ijhydene.2022.08.092

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Wang, F.-K., Amogne, Z. E., Chou, J.-H., & Tseng, C. (2022). Online remaining useful life prediction of lithium-ion batteries using bidirectional long short-term memory with attention mechanism. Energy, 254, 124344. https://doi.org/10.1016/j.energy.2022.124344

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Mateus, B. C., Mendes, M., Farinha, J. T., Assis, R., & Cardoso, A. M. (2021). Comparing lstm and gru models to predict the condition of a pulp paper press. Energies, 14(21), 6958. https://doi.org/10.3390/en14216958

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