As a simple and unsupervised feature learning method, sparse filtering has shown potential in rotating machinery fault diagnosis. However, sparse filtering has the following deficiencies: (a) the optimal sparsity of the learned features cannot be determined. (b) As a shallow network, sparse filtering has a limited capability of learning discriminative features under varying loads. (c) The diagnostic accuracy and robustness are insufficient. To overcome these deficiencies, variant sparse filtering (VSF), which can determine the optimal sparsity, is developed. Then, a deep variant sparse filtering network (DVSFN) is constructed by using stacked VSF to enhance the capability of learning discriminative features. Finally, a novel fault diagnosis method using the DVSFN is presented and verified by using rolling bearing and planetary gearbox datasets. The optimal sparsity of the learned features is determined by parametric analysis. The experimental results show that the DVSFN can adaptively learn discriminative features, irrespective of the varying loads, and the developed diagnostic method can achieve higher testing accuracy and stronger robustness in comparison to classic data-driven methods.

作为一种简单且无监督的特征学习方法，稀疏滤波在旋转机械故障诊断中显示出潜力。然而，稀疏过滤有以下不足：（a）无法确定学习到的特征的最优稀疏度。(b) 作为一个浅层网络，稀疏过滤在不同负载下学习判别特征的能力有限。(c) 诊断准确性和稳健性不足。为了克服这些缺陷，开发了可以确定最佳稀疏度的变体稀疏过滤 (VSF)。然后，通过使用堆叠的 VSF 构建深度变体稀疏过滤网络 (DVSFN)，以增强学习判别特征的能力。最后，提出了一种使用 DVSFN 的新型故障诊断方法，并通过使用滚动轴承和行星齿轮箱数据集进行了验证。学习特征的最佳稀疏性由参数分析确定。实验结果表明，DVSFN 可以自适应地学习判别特征，而不受负载变化的影响，与经典的数据驱动方法相比，所开发的诊断方法可以实现更高的测试精度和更强的鲁棒性。