Domain Adaptation with Multilayer Adversarial Learning for Fault Diagnosis of Gearbox under Multiple Operating Conditions

Abstract

Deep learning has been widely developed to solve fault diagnosis issues, and it is becoming a crucial technology in the modern manufacturing industry. As an important transmission device of mechanical equipment, gearbox often runs at different speeds and loads, which may lead to changes in data distribution for the actual application. The cross-domain problem caused by the different data distribution may decline the performance of the fault diagnosis model based on deep learning. To overcome this challenge, a new domain adaptation method, named MAAN: Multilayer Adversarial Adaptation Networks, for fault diagnosis of gearbox running at multiple operating conditions. The basic framework of our MAAD is a deep convolutional neural network (CNN) and then an adversarial adaptation learning procedure is used for optimizing the basic CNN to adapt cross different domain. The results of the experiment demonstrate that MAAN has outstanding fault diagnosis and domain adaptation capacity, and it could obtain high accuracies for fault diagnosis of the gearbox with changing mode. For investigating the adaptability in this method, we use t-SNE to reduce the high dimension feature for better visualization.