Machinery fault diagnosis is of great significance to improve the reliability of smart manufacturing. Deep learning based fault diagnosis methods have achieved great success. However, the features extracted by different models may vary resulting in ambiguous representation of the data, and even wasted time with manually selecting the optimal hyperparameters. To solve the problems, this paper proposes a new framework named Ensemble Sparse Supervised Model (ESSM), in which a typical deep learning model is treated as two phases of feature learning and model learning. In the feature learning phase, the original data is represented to be a feature matrix as non-redundant as possible by applying sparse filtering. Then, the feature matrix is fed into the model learning phase. Regularization, dropout and rectified linear unit (ReLU) are used in the model's neurons and layers to build a sparse deep neural network. Finally, the output of the sparse deep neural network provides feedback to the first phase to obtain better sparse features. In the proposed method, hyperparameters need to be pre-specified and a python library of talos is employed to finish the process automatically. The proposed method is verified using the bearing data provided by Case Western Reserve University. The result demonstrates that the proposed method can capture the effective pattern of data with the help of sparse constraints and simultaneously provide convenience for the operators with assuring performance.

机械故障诊断对提高智能制造的可靠性具有重要意义。基于深度学习的故障诊断方法取得了巨大的成功。但是，由不同模型提取的特征可能会有所不同，从而导致数据的模棱两可，甚至浪费了手动选择最佳超参数的时间。为了解决这些问题，本文提出了一个新的框架，称为Ensemble Sparse Supervised Model（ESSM），在该框架中，典型的深度学习模型被视为特征学习和模型学习两个阶段。在特征学习阶段，通过应用稀疏过滤将原始数据表示为尽可能不冗余的特征矩阵。然后，将特征矩阵输入模型学习阶段。正则化 模型的神经元和层中使用了辍学和线性校正单元（ReLU）来构建稀疏的深度神经网络。最后，稀疏深度神经网络的输出将反馈提供给第一阶段以获得更好的稀疏特征。在提出的方法中，需要预先指定超参数，并使用talos的python库自动完成此过程。使用凯斯西储大学提供的方位数据验证了该方法的有效性。结果表明，所提出的方法能够在稀疏约束的帮助下捕获有效的数据模式，同时为操作人员提供了保证性能的便利。稀疏深度神经网络的输出为第一阶段提供反馈，以获得更好的稀疏特征。在提出的方法中，需要预先指定超参数，并使用talos的python库自动完成此过程。使用凯斯西储大学提供的方位数据验证了该方法的有效性。结果表明，所提出的方法能够在稀疏约束的帮助下捕获有效的数据模式，同时为操作人员提供了保证性能的便利。稀疏深度神经网络的输出为第一阶段提供反馈，以获得更好的稀疏特征。在提出的方法中，需要预先指定超参数，并使用talos的python库自动完成此过程。使用凯斯西储大学提供的方位数据验证了该方法的有效性。结果表明，所提出的方法能够在稀疏约束的帮助下捕获有效的数据模式，同时为操作人员提供了保证性能的便利。