Deep Model Based Domain Adaptation for Fault Diagnosis

In recent years, machine learning techniques have been widely used to solve many problems for fault diagnosis. However, in many real-world fault diagnosis applications, the distribution of the source domain data (on which the model is trained) is different from the distribution of the target domain data (where the learned model is actually deployed), which leads to performance degradation. In this paper, we introduce domain adaptation, which can find the solution to this problem by adapting the classifier or the regression model trained in a source domain for use in a different but related target domain. In particular, we proposed a novel deep neural network model with domain adaptation for fault diagnosis.…

• NN
  National Natural Science Foundation of China

  Award: 61673239
• NS
  Natural Science Foundation of Guangdong Province

  Award: 2015A030313881