A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis

Fault diagnosis plays an important role in modern industry. With the development of smart manufacturing, the data-driven fault diagnosis becomes hot. However, traditional methods have two shortcomings: 1) their performances depend on the good design of handcrafted features of data, but it is difficult to predesign these features and 2) they work well under a general assumption: the training data and testing data should be drawn from the same distribution, but this assumption fails in many engineering applications. Since deep learning (DL) can extract the hierarchical representation features of raw data, and transfer learning provides a good way to perform a learning task on the different but related…

• NN
  National Natural Science Foundation of China

  Awards: 51775216, 51711530038, 51435009
• HE
  Higher Education Discipline Innovation Project

  Award: B16019