1 Automation and Process Control Engineer, Gist Limited, Bristol, United Kingdom.
2 B. Eng Mechanical Engineering, MSc Engineering and Operations Management, University of New Haven, CT, United States
3 Masters in Electrical Engineering, Research in tactile sensors, robot grasping & manipulation, machine learning, Northeastern University, USA.
4 Graduate Teaching Assistant at The University of Akron, USA
5 General Electric HealthCare, Production Engineer, Noblesville, Indiana, United States.
Research Article
World Journal of Advanced Research and Reviews, 2024, 23(02), 2538-2557
Received on 21 July 2024; revised on 27 August 2024; accepted on 30 August 2024
The integration of Deep Learning (DL), MATLAB, and Advanced Computer- Aided Design (CAD) in the root cause analysis of prognostic errors in Programmable Logic Controller (PLC) systems represents a significant advancement in industrial automation and reliability. This research explores the synergistic application of these technologies to diagnose, predict, and mitigate failures in PLC systems, which are critical for controlling automated processes in various industries. By employing DL algorithms, the study enhances predictive maintenance capabilities, allowing for early detection of anomalies and reducing downtime. MATLAB is utilized as the central platform for data processing, algorithm development, and simulation, providing a versatile environment for integrating DL models with real-time data from PLCs. Advanced CAD tools are employed to model and visualize the physical systems controlled by the PLCs, offering a comprehensive view that bridges the gap between digital analysis and physical implementation. The research methodology includes data collection from PLC systems, DL model training and validation, MATLAB-based simulations, and CAD modelling. The findings demonstrate improved accuracy in identifying the root causes of PLC prognostic errors, leading to more efficient maintenance strategies and enhanced system reliability. This paper concludes that the integration of DL, MATLAB, and CAD provides a powerful approach for advancing predictive maintenance in industrial settings, ultimately contributing to greater operational efficiency and cost savings.
Deep Learning; MATLAB; Advanced CAD; Root Cause Analysis; PLC Systems; Industrial Automation
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Joseph Nnaemeka Chukwunweike, Chikwado Cyril Eze, Ibrahim Abubakar, Lucky Osas Izekor and Adewale Abayomi Adeniran. Integrating deep learning, MATLAB, and advanced CAD for predictive root cause analysis in PLC systems: A multi-tool approach to enhancing industrial automation and reliability. World Journal of Advanced Research and Reviews, 2024, 23(2), 2538-2557. Article DOI: https://doi.org/10.30574/wjarr.2024.23.2.2631