Paper Title
MERGING ARTIFICIAL INTELLIGENCE WITH DIGITAL TWINS FOR FAULT PREDICTION AND CLASSIFICATION USING SUSPENSION’S PRIMARY SPRINGS

Abstract
Abstract - Research on Artificial Intelligence (AI) based equipment failure diagnoses is significant; However, integrating AI with Digital Twins (DTs) remains challenging. The computational methods could not be fault-free due to the complexity of faults and the interaction between a failure and system response for fault positives and negatives. A lack of intelligent management, monitoring, and feedback constitutes a lack of intelligent supervision. An Advanced Digital Models (ADTM) framework with an Artificial Neural Network (ANN) is presented. Combining the ADTM framework with ML through ANN and Support Vector Machines (SVM) tackles these current challenges and overcomes systems’ complex issues. An investigation into a suspension system’s case study demonstrates the viability of ADTMs. For the first time, integration between DTs and ML for monitoring the system’s current and futuristic conditions is fully presented with a prediction error of less than 0.0001 %. The ANN model was improved, classifying the system’s faults with regression accuracy throughout training, validation, and testing were 0.99997, 0.99954, and 0.99931, respectively, for a total accuracy of 0.99986. Keywords - Digital Twin, Hybrid Twin, IoT Healthcare, and Machine Learning