Machine-Learning Approach to Analyze the Status of Forklift Vehicles with Irregular Movement in a Shipyard

TL;DR

This paper presents a machine learning-based system that uses GPS and IoT data to identify and visualize the work status of forklifts in large shipyards, improving equipment management.

Contribution

It introduces a novel approach combining DBSCAN and k-means clustering to determine forklift locations and activities without onsite inspection.

Findings

Effective identification of forklift work zones and activities.

Enhanced visualization of forklift status for management.

Improved resource allocation in shipyards.

Abstract

In large shipyards, the management of equipment, which are used for building a variety of ships, is critical. Because orders vary year to year, shipyard managers are required to determine methods to make the most of their limited resources. A particular difficulty that arises because of the nature and size of shipyards is the management of moving vehicles. In recent years, shipbuilding companies have attempted to manage and track the locations and movements of vehicles using Global Positioning System (GPS) modules. However, because certain vehicles, such as forklifts, roam irregularly around a yard, identifying their working status without being onsite is difficult. Location information alone is not sufficient to determine whether a vehicle is working, moving, waiting, or resting. This study proposes an approach based on machine learning to identify the work status of each forklift. We use the DBSCAN and k-means algorithms to identify the area in which a particular forklift is operating and the type of work it is performing. We developed a business intelligence system to collect information from forklifts equipped with GPS and Internet of Things (IoT) devices. The system provides visual information on the status of individual forklifts and helps in the efficient management of their movements within large shipyards.