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Intelligent Dashboard for Asset Management and Maintenance with Generative AI: a Case Study in Maintenance Engineering

EasyChair Preprint no. 13654

9 pagesDate: June 14, 2024


The integration of Artificial Intelligence (AI) technologies in industrial maintenance engineering is revolutionizing the management and upkeep of assets. This article presents the development of an intelligent dashboard, leveraging generative AI-based chatbots to intuitively and interactively display complex maintenance data. Designed to be trained with extensive databases from a company specialized in asset management, the tool is capable of identifying patterns, forecasting maintenance requirements, and recommending proactive measures. This work introduces an analytical instrument that simplifies the visualization of crucial maintenance indicators and enables specialists to tailor the dashboard to the specific demands of each operational environment. A case study utilizing current data showcases the tool's contribution to enhancing asset management efficiency and fostering sustainable maintenance practices, in line with the advancements of Industry 4.0. Furthermore, this study delves into the role of digital twins, sparking a discourse on the boundaries of the work developed within this area of knowledge.

Keyphrases: Digital Twins, Generative Artificial Intelligence, Industrial Asset Management, Intelligent Dashboard Maintenance, Maintenance Data Analysis

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Rafael Araujo Kluska and Eduardo Rocha Loures and Fernando Deschamps and Lucas Camilotti and Roberto Zanetti Freire and Rodrigo Rotondo},
  title = {Intelligent Dashboard for Asset Management and Maintenance with Generative AI: a Case Study in Maintenance Engineering},
  howpublished = {EasyChair Preprint no. 13654},

  year = {EasyChair, 2024}}
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