This study extensively compares conventional machine learning methods and deep learning for condition monitoring tasks using an AutoML toolbox. The experiments reveal consistent high accuracy in random K-fold cross-validation scenarios 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 methods and neural networks. Conventional methods offer explainability with their modular structure aiding feature identification. In contrast, neural networks require specialized interpretation techniques like occlusion maps to visualize important regions in the input data. Finally, the paper highlights the significance of feature selection, particularly in condition monitoring tasks with limited class variations. Low-complexity models prove sufficient for such tasks, as only a few features from the input signal are typically needed. In summary, these findings offer crucial insights into the strengths and limitations of various approaches, providing valuable benchmarks and identifying the most suitable methods for condition monitoring applications, thereby enhancing their applicability in real-world scenarios.

翻译：本研究广泛比较了传统机器学习方法和深度学习在条件监测任务中的表现，利用自动机器学习工具包进行实验。实验结果显示，在所有测试模型中，随机K折交叉验证场景下均一致取得高精度。然而，当在同一数据集上采用留一组交叉验证时，未出现明显优胜者，表明真实场景中存在领域偏移。此外，研究评估了传统方法和神经网络的可扩展性与可解释性。传统方法因其模块化结构有助于特征识别而具备可解释性；相比之下，神经网络需要特殊的解释技术（如遮挡图）来可视化输入数据中的重要区域。最后，本文强调了特征选择的重要性，尤其是在类别变化有限的条件监测任务中。低复杂度模型足以胜任此类任务，因为通常只需输入信号中的少数几个特征。总之，这些发现为各方法的优势与局限提供了关键见解，为条件监测应用建立了有价值的基准，并识别出最适用的方法，从而提升其在真实场景中的实用性。