Abstract Bearings play a crucial role in machine longevity and is, at the same time, one of the most critical sources of failure in rotor dynamics. Particularly for journal bearings, it is not completely understood how specific damages may influence the response of the rotating system. Consequently, the identification of hydrodynamic bearing faults is challenging. Most of the literature relies on large amounts of training data collections from physical experiments or from the field, which are high in cost. This paper offers a deep learning approach to identify ovalization faults aiming to develop condition monitoring model-based strategies applied to hydrodynamic journal bearings. Therefore, a numerical model was developed to simulate the ovalization fault conditions in order to build training datasets. Afterwards, a deep convolutional neural network algorithm was trained with the generated datasets and used to predict the faults conditions. Finally, the identification performance was evaluated statistically regarding the true-positive identification by both probability density function and subjective logic. The classification accuracy showed promising results for training the machine learning algorithms with simulated data.

中文翻译：

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具有椭圆化故障的滑动轴承深度卷积神经网络诊断中的不确定性量化

摘要 轴承在机器寿命中起着至关重要的作用，同时也是转子动力学中最关键的故障源之一。特别是对于轴颈轴承，尚不完全了解具体损坏如何影响旋转系统的响应。因此，流体动力轴承故障的识别具有挑战性。大多数文献依赖于来自物理实验或现场的大量训练数据收集，成本很高。本文提供了一种识别椭圆化故障的深度学习方法，旨在开发应用于流体动力滑动轴承的基于状态监测模型的策略。因此，开发了一个数值模型来模拟椭圆化故障条件，以建立训练数据集。然后，使用生成的数据集训练深度卷积神经网络算法并用于预测故障条件。最后，通过概率密度函数和主观逻辑对真阳性识别的识别性能进行统计评估。分类准确性显示了使用模拟数据训练机器学习算法的有希望的结果。