Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 268-272 |
| Seitenumfang | 5 |
| Fachzeitschrift | Tehnicki Glasnik |
| Jahrgang | 18 |
| Ausgabenummer | 2 |
| Publikationsstatus | Veröffentlicht - 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
- Betriebswirtschaft, Management und Rechnungswesen (insg.)
- Management-Informationssysteme
- Informatik (insg.)
- Information systems
- Ingenieurwesen (insg.)
- Informatik (insg.)
- Angewandte Informatik
- Informatik (insg.)
- Computergrafik und computergestütztes Design
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in: Tehnicki Glasnik, Jahrgang 18, Nr. 2, 31.05.2024, S. 268-272.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Failure Prediction of Automated Guided Vehicle Systems in Production Environments through Artificial Intelligence
AU - Li, Li
AU - Schulze, Lothar
N1 - Publisher Copyright: © 2024 University North. All rights reserved.
PY - 2024/5/31
Y1 - 2024/5/31
N2 - 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.
AB - 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.
KW - Artificial Intelligence
KW - Automated Guided Vehicle
KW - Forecasting
KW - Long Short-Term Memory
KW - TensorFlow
KW - Time-Series Analysis
UR - http://www.scopus.com/inward/record.url?scp=85193974482&partnerID=8YFLogxK
U2 - 10.31803/tg-20240416185206
DO - 10.31803/tg-20240416185206
M3 - Article
AN - SCOPUS:85193974482
VL - 18
SP - 268
EP - 272
JO - Tehnicki Glasnik
JF - Tehnicki Glasnik
SN - 1846-6168
IS - 2
ER -