Learn Generalization Feature via Convolutional Neural Network: A Fault Diagnosis Scheme Toward Unseen Operating Conditions

Abstract

In recent years, Convolutional neural networks (CNNs) have achieved start-of-art performance in the fault diagnosis field. If there is no available information on the unseen operating conditions, the model trained on the seen operating condition cannot perform well. One of the feasible strategies is to enhance the generalization ability of the network on various seen operating conditions. We introduce the center loss to the traditional CNN and build an end-to-end fault diagnosis framework (called CNN-C). By minimizing the intra-class variations, center loss cluster the learned features across various seen operating conditions. With the joint supervision of the center loss and the softmax loss, the learned features of the same class could minimize the domain difference across various seen operating conditions while the features of different classes are separable. The generalization ability of network is improved on unseen operating conditions. Compared with the shallow methods and traditional CNN, the proposed method is promising to deal with the fault diagnosis tasks of the bearing and gearbox. INDEX TERMS Convolutional neural network, center loss, unseen operating condition, fault diagnosis, feature generalization.

A New Adversarial Domain Generalization Network Based on Class Boundary Feature Detection for Bearing Fault Diagnosis

“…Li et al [24] obtained generic features by adversarial training and achieved high diagnostic accuracy for the diagnosis of unknown operating environments. Yang et al [25] minimized the domain differences by jointly monitoring the central loss and softmax loss. Han et al [26] considered intrinsic and extrinsic generalization goals and proposed a hybrid diagnostic network.…”