Failure Prediction of Automated Guided Vehicle Systems in Production Environments through Artificial Intelligence

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autoren

  • Li Li
  • Lothar Schulze

Externe Organisationen

  • Technische Hochschule Ostwestfalen-Lippe
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Seiten (von - bis)268-272
Seitenumfang5
FachzeitschriftTehnicki Glasnik
Jahrgang18
Ausgabenummer2
PublikationsstatusVeröffentlicht - 31 Mai 2024

Abstract

Modern industrial systems demand intricate connectivity and automation, especially in the realm of shop floor processes and intralogistics. Automated Guided Vehicle (AGV) systems are characterized by their potential for seamlessly networking value creation areas. However, failures and disruptions in AGV systems and adjacent facilities can lead to production halts, adversely affecting delivery reliability and quality. A substantial portion of the downtime stems from manual troubleshooting, underscoring the pivotal importance of the response time from maintenance staff. This paper introduces an approach employing a neural network with long short-term memory for forecasting and predictive maintenance to enhance AGV system reliability and availability in production environments. By analysing historical data, identifying patterns, and predicting potential failures or maintenance needs in AGV components and neighbouring facilities, the proposed AI-based forecasting ensures timely preventive measures. A case study shows the effectiveness of this approach in significantly improving AGV system performance, minimizing disruptions, and enhancing operational availability. This research contributes to smart manufacturing by providing a practical solution for optimizing availability of the concerned AGV system through advanced AI-based forecasting strategies.

ASJC Scopus Sachgebiete

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Failure Prediction of Automated Guided Vehicle Systems in Production Environments through Artificial Intelligence. / Li, Li; Schulze, Lothar.
in: Tehnicki Glasnik, Jahrgang 18, Nr. 2, 31.05.2024, S. 268-272.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Li L, Schulze L. Failure Prediction of Automated Guided Vehicle Systems in Production Environments through Artificial Intelligence. Tehnicki Glasnik. 2024 Mai 31;18(2):268-272. doi: 10.31803/tg-20240416185206
Li, Li ; Schulze, Lothar. / Failure Prediction of Automated Guided Vehicle Systems in Production Environments through Artificial Intelligence. in: Tehnicki Glasnik. 2024 ; Jahrgang 18, Nr. 2. S. 268-272.
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