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Abstract：
Intelligent fault diagnostics based on deep learning provides a favorable guarantee for the reliable operation of equipment, but a trained deep learning model generally has low prediction accuracy in cross-domain diagnostics. To solve this problem, a deep learning fault diagnosis method based on the reconstructed envelope spectrum is proposed to improve the ability of rolling bearing cross-domain fault diagnostics in this paper. First, based on the envelope spectrum morphology of rolling bearing failures, a standard envelope spectrum is constructed that reveals the unique characteristics of different bearing health states and eliminates the differences between domains due to different bearing speeds and bearing models. Then, a fault diagnosis model was constructed using a convolutional neural network to learn features and complete fault classification. Finally, using two publicly available bearing data sets and one bearing data set obtained by self-experimentation, the proposed method is applied to the data of the fault diagnostics of rolling bearings under different rotational speeds and different bearing types. The experimental results show that, compared with some popular feature extraction methods, the proposed method can achieve high diagnostic accuracy with data at different rotational speeds and different bearing types, and it is an effective method for solving the problem with cross-domain fault diagnostics for rolling bearings.
摘要：
基于深度学习的智能故障诊断为设备的可靠运行提供了有利保障,但经过训练的深度学习模型在跨域诊断中的预测精度通常较低。为了解决这个问题,为了提高滚动轴承跨域故障诊断能力,提出了一种基于重构包络谱的深度学习故障诊断方法。首先,基于滚动轴承故障的包络谱形态，构建了一个标准的包络谱，揭示了不同轴承健康状态的独特特征，并消除了由于不同轴承速度和轴承模型而导致的域之间的差异。然后，利用卷积神经网络建立故障诊断模型，学习特征并完成故障分类。最后,使用两个公开可用的轴承数据集和一个通过自我实验获得的轴承数据集，将该方法应用于不同转速和不同轴承类型下的滚动轴承故障诊断数据。实验结果表明,与一些流行的特征提取方法相比，该方法可以在不同转速和不同轴承类型的数据下实现较高的诊断精度，是解决滚动轴承跨域故障诊断问题的有效方法。
