Speaker
Description
Journal bearings are prevalent engineering components in almost all types of rotating machinery. Shaft misalignment is one of the most common defects observed in rotating systems and has a substantial effect on dynamic behavior, stability and lifetime. Aim of this study is the binary identification of misalignment using a variety of Machine Learning techniques. Nevertheless, the limited quantity of provided data points coupled with the substantial imbalance between the aligned and misaligned cases necessitated the implementation of oversampling and data augmentation methods. The utilization of SMOTE-LOF for oversampling the minority class, alongside the adoption of a Conditional Tabular GAN for the generation of synthetic data points yielded substantial outcomes. Among the augmented datasets, it was observed that the dataset comprising 5000 synthetic samples exhibited the highest quality score compared to the initial dataset, while the dataset with 4000 generated samples had the best performance on the Machine Learning algorithms.
| Conference Topic Areas | Track3: Computational Mechanics and Structural Integrity |
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