The forecast of the AGV battery discharging via the machine learning methods

Olena Pavliuk; Tomasz Steclik; Piotr Biernacki

doi:10.1109/BigData55660.2022.10020968

The forecast of the AGV battery discharging via the machine learning methods

Wyniki badań: Rozdział w książce/raport/materiał konferencyjny › Wkład w konferencję › recenzja

10 Cytowania z bazy Scopus

Abstrakt

We reviewed the existing and currently used approach in processing the residual charge of an AGV battery. The method of setting up the experiment for collecting the historical data for an AGV Formica 1 of the AIUT company was proposed and implemented. The collected properties of the time series were analyzed and the algorithm for the necessary data pre-processing was selected. This algorithm includes padding any the suppression spontaneous peaks, the recovery of any lost data and data normalization.The collected data for the AGVs were also analyzed using the correlation analysis methods (Pearson, Spearman and Kendall correlations). These determined the parameters on which the AGV battery discharge depends. A battery discharge prediction approach that is based on the quasi-stochastic signal's probabilistic characteristics is suggested.A Multiparameter ANN model using a time window was developed. The dependence of the forecast error on the length of the time window was also investigated. The optimal parameters of the ANN were selected experimentally. The mean absolute percentage error for the AGV short-term forecast of a battery discharging was less than 1%. For the other parameters on which it depends, the AGV battery discharging was less than 9%. All of the studies were conducted within the framework of the "Automated Guided Vehicles integrated with Collaborative Robots for Smart Industry Perspective"project.

Język oryginału	angielski
Tytuł publikacji goszczącej	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
Redaktorzy	Shusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
Wydawca	Institute of Electrical and Electronics Engineers Inc.
Strony	6315-6324
Liczba stron	10
ISBN (elektroniczny)	9781665480451
Identyfikatory DOI	https://doi.org/10.1109/BigData55660.2022.10020968
Status publikacji	Opublikowano - 2022
Wydarzenie	2022 IEEE International Conference on Big Data, Big Data 2022 - Osaka, Japonia Czas trwania: 17 gru 2022 → 20 gru 2022

Seria publikacji

Nazwa	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

Konferencja

Konferencja	2022 IEEE International Conference on Big Data, Big Data 2022
Kraj/Terytorium	Japonia
Miejscowość	Osaka
Okres	17/12/22 → 20/12/22

Cele SDG ONZ

Ten wynik przyczynia się do realizacji następujących celów zrównoważonego rozwoju

Cel 9 Przemysł, innowacje i infrastruktura

Obszary tematyczne ASJC Scopus

Modelowanie i symulacja
Sieci komputerowe i komunikacja
Systemy informacyjne
Systemy informacyjne i zarządzanie
Bezpieczeństwo, ryzyko, niezawodność i jakość
Sterowanie i optymalizacja

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10.1109/BigData55660.2022.10020968

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Link do publikacji w Scopus

Cytowanie

Pavliuk, O., Steclik, T., & Biernacki, P. (2022). The forecast of the AGV battery discharging via the machine learning methods. W S. Tsumoto, Y. Ohsawa, L. Chen, D. Van den Poel, X. Hu, Y. Motomura, T. Takagi, L. Wu, Y. Xie, A. Abe, & V. Raghavan (Red.), Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022 (str. 6315-6324). (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData55660.2022.10020968

Pavliuk, Olena ; Steclik, Tomasz ; Biernacki, Piotr. / The forecast of the AGV battery discharging via the machine learning methods. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. redaktor / Shusaku Tsumoto ; Yukio Ohsawa ; Lei Chen ; Dirk Van den Poel ; Xiaohua Hu ; Yoichi Motomura ; Takuya Takagi ; Lingfei Wu ; Ying Xie ; Akihiro Abe ; Vijay Raghavan. Institute of Electrical and Electronics Engineers Inc., 2022. str. 6315-6324 (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022).

@inproceedings{a503e4501333449dbbc1ce66a86cf73a,

title = "The forecast of the AGV battery discharging via the machine learning methods",

abstract = "We reviewed the existing and currently used approach in processing the residual charge of an AGV battery. The method of setting up the experiment for collecting the historical data for an AGV Formica 1 of the AIUT company was proposed and implemented. The collected properties of the time series were analyzed and the algorithm for the necessary data pre-processing was selected. This algorithm includes padding any the suppression spontaneous peaks, the recovery of any lost data and data normalization.The collected data for the AGVs were also analyzed using the correlation analysis methods (Pearson, Spearman and Kendall correlations). These determined the parameters on which the AGV battery discharge depends. A battery discharge prediction approach that is based on the quasi-stochastic signal's probabilistic characteristics is suggested.A Multiparameter ANN model using a time window was developed. The dependence of the forecast error on the length of the time window was also investigated. The optimal parameters of the ANN were selected experimentally. The mean absolute percentage error for the AGV short-term forecast of a battery discharging was less than 1\%. For the other parameters on which it depends, the AGV battery discharging was less than 9\%. All of the studies were conducted within the framework of the {"}Automated Guided Vehicles integrated with Collaborative Robots for Smart Industry Perspective{"}project.",

keywords = "AGV, ANN, Battery voltage, Correlation, Data mining, Forecast, Lost data, Machine Learning",

author = "Olena Pavliuk and Tomasz Steclik and Piotr Biernacki",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Big Data, Big Data 2022 ; Conference date: 17-12-2022 Through 20-12-2022",

year = "2022",

doi = "10.1109/BigData55660.2022.10020968",

language = "English",

series = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "6315--6324",

editor = "Shusaku Tsumoto and Yukio Ohsawa and Lei Chen and \{Van den Poel\}, Dirk and Xiaohua Hu and Yoichi Motomura and Takuya Takagi and Lingfei Wu and Ying Xie and Akihiro Abe and Vijay Raghavan",

booktitle = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",

address = "United States",

}

Pavliuk, O , Steclik, T & Biernacki, P 2022, The forecast of the AGV battery discharging via the machine learning methods. w S Tsumoto, Y Ohsawa, L Chen, D Van den Poel, X Hu, Y Motomura, T Takagi, L Wu, Y Xie, A Abe & V Raghavan (red), Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022, Institute of Electrical and Electronics Engineers Inc., str. 6315-6324, 2022 IEEE International Conference on Big Data, Big Data 2022, Osaka, Japonia, 17/12/22. https://doi.org/10.1109/BigData55660.2022.10020968

The forecast of the AGV battery discharging via the machine learning methods. / Pavliuk, Olena ; Steclik, Tomasz ; Biernacki, Piotr.
Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. red. / Shusaku Tsumoto; Yukio Ohsawa; Lei Chen; Dirk Van den Poel; Xiaohua Hu; Yoichi Motomura; Takuya Takagi; Lingfei Wu; Ying Xie; Akihiro Abe; Vijay Raghavan. Institute of Electrical and Electronics Engineers Inc., 2022. str. 6315-6324 (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022).

Wyniki badań: Rozdział w książce/raport/materiał konferencyjny › Wkład w konferencję › recenzja

TY - GEN

T1 - The forecast of the AGV battery discharging via the machine learning methods

AU - Pavliuk, Olena

AU - Steclik, Tomasz

AU - Biernacki, Piotr

PY - 2022

Y1 - 2022

N2 - We reviewed the existing and currently used approach in processing the residual charge of an AGV battery. The method of setting up the experiment for collecting the historical data for an AGV Formica 1 of the AIUT company was proposed and implemented. The collected properties of the time series were analyzed and the algorithm for the necessary data pre-processing was selected. This algorithm includes padding any the suppression spontaneous peaks, the recovery of any lost data and data normalization.The collected data for the AGVs were also analyzed using the correlation analysis methods (Pearson, Spearman and Kendall correlations). These determined the parameters on which the AGV battery discharge depends. A battery discharge prediction approach that is based on the quasi-stochastic signal's probabilistic characteristics is suggested.A Multiparameter ANN model using a time window was developed. The dependence of the forecast error on the length of the time window was also investigated. The optimal parameters of the ANN were selected experimentally. The mean absolute percentage error for the AGV short-term forecast of a battery discharging was less than 1%. For the other parameters on which it depends, the AGV battery discharging was less than 9%. All of the studies were conducted within the framework of the "Automated Guided Vehicles integrated with Collaborative Robots for Smart Industry Perspective"project.

AB - We reviewed the existing and currently used approach in processing the residual charge of an AGV battery. The method of setting up the experiment for collecting the historical data for an AGV Formica 1 of the AIUT company was proposed and implemented. The collected properties of the time series were analyzed and the algorithm for the necessary data pre-processing was selected. This algorithm includes padding any the suppression spontaneous peaks, the recovery of any lost data and data normalization.The collected data for the AGVs were also analyzed using the correlation analysis methods (Pearson, Spearman and Kendall correlations). These determined the parameters on which the AGV battery discharge depends. A battery discharge prediction approach that is based on the quasi-stochastic signal's probabilistic characteristics is suggested.A Multiparameter ANN model using a time window was developed. The dependence of the forecast error on the length of the time window was also investigated. The optimal parameters of the ANN were selected experimentally. The mean absolute percentage error for the AGV short-term forecast of a battery discharging was less than 1%. For the other parameters on which it depends, the AGV battery discharging was less than 9%. All of the studies were conducted within the framework of the "Automated Guided Vehicles integrated with Collaborative Robots for Smart Industry Perspective"project.

KW - AGV

KW - ANN

KW - Battery voltage

KW - Correlation

KW - Data mining

KW - Forecast

KW - Lost data

KW - Machine Learning

UR - https://www.scopus.com/pages/publications/85147899180

U2 - 10.1109/BigData55660.2022.10020968

DO - 10.1109/BigData55660.2022.10020968

M3 - Conference contribution

AN - SCOPUS:85147899180

T3 - Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

SP - 6315

EP - 6324

BT - Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

A2 - Tsumoto, Shusaku

A2 - Ohsawa, Yukio

A2 - Chen, Lei

A2 - Van den Poel, Dirk

A2 - Hu, Xiaohua

A2 - Motomura, Yoichi

A2 - Takagi, Takuya

A2 - Wu, Lingfei

A2 - Xie, Ying

A2 - Abe, Akihiro

A2 - Raghavan, Vijay

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE International Conference on Big Data, Big Data 2022

Y2 - 17 December 2022 through 20 December 2022

ER -

Pavliuk O , Steclik T , Biernacki P. The forecast of the AGV battery discharging via the machine learning methods. W Tsumoto S, Ohsawa Y, Chen L, Van den Poel D, Hu X, Motomura Y, Takagi T, Wu L, Xie Y, Abe A, Raghavan V, redaktorzy, Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. Institute of Electrical and Electronics Engineers Inc. 2022. str. 6315-6324. (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022). doi: 10.1109/BigData55660.2022.10020968

The forecast of the AGV battery discharging via the machine learning methods

Abstrakt

Seria publikacji

Konferencja

Cele SDG ONZ

Obszary tematyczne ASJC Scopus

Uzyskaj dostęp do dokumentu

Inne pliki i linki

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