Feature learning is an integral part of intelligent fault diagnosis. Sparse feature learning methods have been shown to be effective in learning discriminative features. To learn features with optimal sparsity distribution, an unsupervised sparse feature learning method called variant sparse filtering is developed. Variant sparse filtering uses a sparsity parameter to determine the optimal sparse feature distribution. A three-stage fault diagnosis method based on variant sparse filtering is then developed to identify rotating machinery faults. The method is validated using a rolling bearing dataset and a planetary gearbox dataset and is compared with other diagnosis methods. The results show that the developed diagnosis method can identify single faults and compound faults with high accuracy.

中文翻译：

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使用无监督稀疏特征学习方法的智能故障诊断

特征学习是智能故障诊断的组成部分。稀疏特征学习方法已被证明在学习鉴别特征方面是有效的。为了学习具有最佳稀疏分布的特征，开发了一种称为变异稀疏滤波的无监督稀疏特征学习方法。变体稀疏过滤使用稀疏性参数来确定最佳的稀疏特征分布。然后，提出了一种基于变式稀疏滤波的三阶段故障诊断方法，以识别旋转机械故障。该方法使用滚动轴承数据集和行星齿轮箱数据集进行了验证，并与其他诊断方法进行了比较。结果表明，所开发的诊断方法能够准确识别单个故障和复合故障。