Details
| Original language | English |
|---|---|
| Pages (from-to) | 268-272 |
| Number of pages | 5 |
| Journal | Tehnicki Glasnik |
| Volume | 18 |
| Issue number | 2 |
| Publication status | Published - 31 May 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.
Keywords
- Artificial Intelligence, Automated Guided Vehicle, Forecasting, Long Short-Term Memory, TensorFlow, Time-Series Analysis
ASJC Scopus subject areas
- Business, Management and Accounting(all)
- Management Information Systems
- Computer Science(all)
- Information Systems
- Engineering(all)
- Computer Science(all)
- Computer Science Applications
- Computer Science(all)
- Computer Graphics and Computer-Aided Design
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In: Tehnicki Glasnik, Vol. 18, No. 2, 31.05.2024, p. 268-272.
Research output: Contribution to journal › Article › Research › 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 -