Ensemble sparse supervised model for bearing fault diagnosis in smart manufacturing

Abstract 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.