Version 1
: Received: 21 June 2024 / Approved: 21 June 2024 / Online: 24 June 2024 (18:41:51 CEST)
How to cite:
Ammar, M. Advanced Digital Twins for Current Real Time Condition Monitoring, Diagnosis and Predictive Remaining Lifecycles. Preprints2024, 2024061558. https://doi.org/10.20944/preprints202406.1558.v1
Ammar, M. Advanced Digital Twins for Current Real Time Condition Monitoring, Diagnosis and Predictive Remaining Lifecycles. Preprints 2024, 2024061558. https://doi.org/10.20944/preprints202406.1558.v1
Ammar, M. Advanced Digital Twins for Current Real Time Condition Monitoring, Diagnosis and Predictive Remaining Lifecycles. Preprints2024, 2024061558. https://doi.org/10.20944/preprints202406.1558.v1
APA Style
Ammar, M. (2024). Advanced Digital Twins for Current Real Time Condition Monitoring, Diagnosis and Predictive Remaining Lifecycles. Preprints. https://doi.org/10.20944/preprints202406.1558.v1
Chicago/Turabian Style
Ammar, M. 2024 "Advanced Digital Twins for Current Real Time Condition Monitoring, Diagnosis and Predictive Remaining Lifecycles" Preprints. https://doi.org/10.20944/preprints202406.1558.v1
Abstract
The implementation of systems condition monitoring is of utmost importance in ensuring effective maintenance, optimal efficiency, and adherence to established design criteria. This research presents a novel and economically efficient Digital Twin Model (DTM) designed for the purpose of Live Condition Monitoring (LCM) and Predictive Lifecycles. This study discusses the enhancement of DTs modelling by decreasing the number of dimensions from five to three: Physical, Digital, and Connection entities. The resulting model exhibits a notable improvement in accuracy and efficiency. The suggested Dynamic Time Model (DTM) enhances the empirical pre-set average load for both simulation and experimentation by 35.7%, which is equivalent to a 1.6-fold increase. The DTM demonstrates a significant enhancement in the empirical predefined average lifecycles of the system, as shown in the real-life case study. Specifically, the DTM achieves a 12-fold improvement compared to the simulated results and a nine-fold improvement compared to the experimental findings. The suggested Dynamic Time Management (DTM) demonstrates a significant enhancement in the average lifecycles of the system, with a 19.7% improvement (equivalent to 1.2 times greater) compared to the wireless DTM results obtained through the application of the real system load. The suggested Dynamic Time Warping (DTW) method's accuracy and efficiency are shown via a practical case study using the suspension system of a Peugeot 3008 vehicle.
Keywords
Industry 4.0; Digital Twin; Predictive Maintenance; Condition Monitoring; virtual reality
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
