Research Area:  Internet of Things

Abstract:

first_pagesettingsOrder Article Reprints Open AccessArticle Multimodal Large Language Model-Based Fault Detection and Diagnosis in Context of Industry 4.0 by Khalid M. Alsaif *ORCID,Aiiad A. AlbeshriORCID,Maher A. KhemakhemORCID andFathy E. Eassa Department of Computer Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia * Author to whom correspondence should be addressed. Electronics 2024, 13(24), 4912; https://doi.org/10.3390/electronics13244912 Submission received: 11 November 2024 / Revised: 3 December 2024 / Accepted: 9 December 2024 / Published: 12 December 2024 (This article belongs to the Special Issue Advances in Large Language Model Empowered Machine Learning: Design and Application) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract In this paper, a novel multimodal large language model-based fault detection and diagnosis framework that addresses the limitations of traditional fault detection and diagnosis approaches is proposed. The proposed framework leverages the Generative Pre-trained Transformer-4-Preview model to improve its scalability, generalizability, and efficiency in handling complex systems and various fault scenarios. Moreover, synthetic datasets generated via large language models augment the knowledge base and enhance the accuracy of fault detection and diagnosis of imbalanced scenarios. In the framework, a hybrid architecture that integrates online and offline processing, combining real-time data streams with fine-tuned large language models for dynamic, accurate, and context-aware fault detection suited to industrial settings, particularly focusing on security concerns, is introduced. This comprehensive approach aims to address traditional fault detection and diagnosis challenges and advance the field toward more adaptive and efficient fault diagnosis systems. This paper presents a detailed literature review, including a detailed taxonomy of fault detection and diagnosis methods and their applications across various industrial domains. This study discusses case study results and model comparisons, exploring the implications for future developments in industrial fault detection and diagnosis systems within Industry 4.0 technologies.

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Author(s) Name:  Khalid M. Alsaif ,Aiiad A. Albeshri,Maher A. Khemakhem and Fathy E. Eassa

Publisher name:  MDPI

DOI:  10.3390/electronics13244912

Volume Information:  Volume 13, (2024)

Paper Link:   https://www.mdpi.com/2079-9292/13/24/4912