Preliminary communication

https://doi.org/10.31803/tg-20240416185206

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

Li Li ; Technische Hochschule Ostwestfalen-Lippe, Campusallee 12, 32657 Lemgo, Germany *
Lothar Schulze ; Leibniz Universität Hannover, Callin str. 36, 30167 Hannover, Germany

* Corresponding author.

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; Long Short-Term Memory; TensorFlow; Time-Series Analysis and Forecasting

Hrčak ID:

316910

URI

https://hrcak.srce.hr/316910

Publication date:

31.5.2024.

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