Ozhan Gecgel

TL;DR: A numerical model was developed to simulate the ovalization fault conditions in order to build training datasets and a deep convolutional neural network algorithm was trained with the generated datasets and used to predict the faults conditions.

TL;DR: An ML and DL classification performance comparison of several algorithms to diagnose faults in a gearbox based on realistic simulated vibration data revealed the superiority of Convolutional Neural Network compared to other classifiers.

TL;DR: A simulation-driven ML framework to estimate the crack size in a gear tooth using simulated vibrations signals is proposed and it was shown that the Decision Tree Classifier (DTC) performed best in the identification of small crack sizes regardless of the random selection of the training subsample.

TL;DR: This work develops a framework using a deep learning algorithm to classify wear faults in hydrodynamic journal bearings using simulated vibrations signals and shows that the proposed framework can be a promising tool to wear fault diagnostics in journal bearings.

TL;DR: To address the impact of uncertainty, quality and quantity of data, physics-based machine learning (PBML), and digital twin (DT) in diagnostics and prognostics, four research questions were formulated and the methodology used was Preferred Reporting Items for Systematic Review and Meta-Analysis.