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Classifier ensembles to improve the robustness to noise of bearing fault diagnosis

机译：集成分类器以提高轴承故障诊断的抗噪声能力

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摘要

In this paper, we perform a noise analysis to assess the degree of robustness to noise of a neural classifier aimed at performing multi-class diagnosis of rolling element bearings. We work on vibration signals collected by means of two accelerometers and we consider ten levels of noise, each of which characterized by a different signal-to-noise ratio ranging from 40.55 to -11.35 db. We classify the noisy signals by means of a neural classifier initially trained on signals without noise and then we repeat the training process with signals affected by increasing levels of noise. We show that adding noisy signals to the training set we can significantly increase the classification accuracy of a single classifier. Finally, we apply the two most used strategies to combine classifiers: classifier fusion and classifier selection, and show that, in both cases, we can significantly increase the performance of the single best classifier. In particular, classifier selection achieves the best results for low and medium levels of noise, while classifier fusion is the most accurate for high levels of noise. The analysis presented in the paper can be profitably used to identify both the type of classifier (e.g., single classifier or classifier ensemble) and how many and which noise levels should be used in the training phase in order to achieve the desired classification accuracy in the application domain of interest.
机译：在本文中，我们进行了噪声分析以评估神经分类器对噪声的鲁棒性程度，该神经分类器旨在对滚动轴承进行多类诊断。我们处理通过两个加速度计收集的振动信号，并考虑了十个级别的噪声，每个级别的特征是信噪比从40.55到-11.35 db不等。我们使用神经分类器对噪声信号进行分类，该神经分类器最初在无噪声的信号上进行训练，然后对受噪声水平增加影响的信号重复训练过程。我们表明，将噪声信号添加到训练集可以显着提高单个分类器的分类精度。最后，我们应用两种最常用的策略来组合分类器：分类器融合和分类器选择，并表明在两种情况下，我们都可以显着提高单个最佳分类器的性能。尤其是，分类器选择在低和中等噪声水平下可获得最佳结果，而分类器融合对于高噪声水平最为准确。本文提出的分析可以有益地用来识别分类器的类型（例如单分类器或分类器集合）以及在训练阶段应该使用多少噪声水平以及在训练阶段达到期望的分类精度。感兴趣的应用程序域。

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