Deep neural networks (DNNs) have shown potential in intelligent fault diagnosis of rotating machinery. However, traditional DNNs such as the back-propagation neural network are highly sensitive to the initial weights and easily fall into the local optimum, which restricts the feature learning capability and diagnostic performance. To overcome the above problems, a deep sparse filtering network (DSFN) constructed by stacked sparse filtering is developed in this paper and applied to fault diagnosis. The developed DSFN is pre-trained by sparse filtering in an unsupervised way. The back-propagation algorithm is employed to optimize the DSFN after pre-training. Then, the DSFN-based intelligent fault diagnosis method is validated using two experiments. The results show that pre-training with sparse filtering and fine-tuning can help the DSFN search for the optimal network parameters, and the DSFN can learn discriminative features adaptively from rotating machinery datasets. Compared with classical methods, the developed diagnostic method can diagnose rotating machinery faults with higher accuracy using fewer training samples.

深度神经网络（DNN）在旋转机械的智能故障诊断中已显示出潜力。然而，传统的DNN（例如，反向传播神经网络）对初始权重高度敏感，并且容易陷入局部最优状态，这限制了特征学习能力和诊断性能。为了克服上述问题，本文开发了一种由堆叠式稀疏滤波构成的深度稀疏滤波网络（DSFN），并将其应用于故障诊断。所开发的DSFN通过稀疏滤波以无监督的方式进行了预训练。预训练后，采用反向传播算法优化DSFN。然后，通过两个实验验证了基于DSFN的智能故障诊断方法。结果表明，采用稀疏滤波和微调的预训练可以帮助DSFN搜索最佳网络参数，并且DSFN可以从旋转机械数据集中自适应地学习判别特征。与经典方法相比，开发的诊断方法可以使用更少的训练样本来更高精度地诊断旋转机械故障。