Spall type classification, a task in which the type of the spall is estimated, is an important stage for bearing diagnosis and prognosis. Many machine learning algorithms have been suggested for spall type classification. However, they are not relevant for diagnosing critical rotating machinery, where very few, if any, faulty examples (labeled and unlabeled) are available due to safety considerations. In this study, a novel hybrid algorithm is proposed, which enables the classification of the spall type based on zero-fault-shot learning. The novel algorithm combines physics-based algorithms together with machine learning to overcome the lack of faulty data. It projects the signals into an invariant feature space by physics-based algorithms and classifies the spall type by a fully connected neural network. The new algorithm is demonstrated on several well-known experimental datasets and significantly improves the performance of currently available learning algorithms for zero-fault-shot learning. It improves the results of the state-of-the-art algorithm from at most 60% on the six tested datasets to at least 98% accuracy on all tested datasets with the newly suggested algorithm.

中文翻译：

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基于混合方法的轴承剥落类型分类的零故障学习


剥落类型分类是估计剥落类型的任务，是轴承诊断和预后的重要阶段。已经建议将许多机器学习算法用于剥落类型分类。然而，它们与诊断关键旋转机械无关，出于安全考虑，很少有（如果有）有缺陷的例子（有标签和无标签）。在这项研究中，提出了一种新的混合算法，该算法能够基于零故障射击学习对剥落类型进行分类。这种新颖的算法将基于物理的算法与机器学习相结合，以克服缺乏错误数据的问题。它通过基于物理的算法将信号投影到不变特征空间中，并通过完全连接的神经网络对剥落类型进行分类。新算法在几个著名的实验数据集上进行了演示，并显著提高了当前可用的零错误学习算法的性能。它使用新建议的算法，将最先进算法的结果从 6 个测试数据集上的最高 60% 提高到所有测试数据集上至少 98% 的准确率。