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This study extensively compares conventional machine learning methods anddeep learning for condition monitoring tasks using an AutoML toolbox. Theexperiments reveal consistent high accuracy in random K-fold cross-validationscenarios across all tested models. However, when employing leave-one-group-out(LOGO) cross-validation on the same datasets, no clear winner emerges,indicating the presence of domain shift in real-world scenarios. Additionally,the study assesses the scalability and interpretability of conventional methodsand neural networks. Conventional methods offer explainability with theirmodular structure aiding feature identification. In contrast, neural networksrequire specialized interpretation techniques like occlusion maps to visualizeimportant regions in the input data. Finally, the paper highlights thesignificance of feature selection, particularly in condition monitoring taskswith limited class variations. Low-complexity models prove sufficient for suchtasks, as only a few features from the input signal are typically needed. Insummary, these findings offer crucial insights into the strengths andlimitations of various approaches, providing valuable benchmarks andidentifying the most suitable methods for condition monitoring applications,thereby enhancing their applicability in real-world scenarios.

Comparing AutoML and Deep Learning Methods for Condition Monitoring using Realistic Validation Scenarios