Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal International Journal of Reconfigurable and Embedded Systems (IJRES)
Srikanth Kilaru
Vignan’s Nirula Institute of Technology and Science for Women
Economics, Econometrics & Finance

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Intelligent deep learning models for fault diagnosis in sixth generation industrial internet of things environments Hareesha Dandamudi; Chenchu Punnarao Bandi; Simhadri Mallikarjuna Rao; Palacharla SVS Sridhar; Mythili Murugan; Srikanth Kilaru; Rama Krishna Paladugu
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 15, No 2: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v15.i2.pp281-290

Abstract

The integration of sixth-generation (6G) communication and Industry 4.0 technologies has transformed industrial automation, connectivity, and intelligent data analysis. However, the increasing volume and diversity of data generated from multiple industrial sources create significant challenges for accurate and real-time fault detection. This study presents a deep learning-based framework designed to improve fault identification in 6G-enabled Industry 4.0 environments. The proposed system processes heterogeneous data collected from internet of things (IoT) devices, monitoring sensors, and automated industrial equipment to ensure reliable and scalable fault analysis. A hybrid model combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks is implemented to capture spatial features and temporal relationships within industrial datasets. The framework also focuses on optimizing computational resources while maintaining high detection performance. Simulation-based evaluations demonstrate that the proposed approach enhances fault detection accuracy and system reliability, making it suitable for advanced smart manufacturing and industrial monitoring applications.
Co-Authors Chenchu Punnarao Bandi Hareesha Dandamudi Mythili Murugan Palacharla SVS Sridhar Rama Krishna Paladugu Simhadri Mallikarjuna Rao