MSIFT: A novel end-to-end mechanical fault diagnosis framework under limited & imbalanced data using multi-source information fusion

Data-driven intelligent fault diagnosis methods have emerged as powerful tools for monitoring and maintaining the operating conditions of mechanical equipment. However, in real-world engineering scenarios, mechanical equipment typically operates under normal conditions, resulting in limited and imbalanced (L&I) data. This situation gives rise to label bias and biased training. Meanwhile, the current multi-source information fault diagnosis research to date has tended to focus on fault identification rather than effective feature fusion strategies. To solve these issues, a novel end-to-end mechanical fault diagnosis framework under limited & imbalanced data using multi-source information fusion is proposed to…

• CS
  China Scholarship Council

  Award: CSC N202308130067
• HM
  H2020 Marie Skłodowska-Curie Actions
• HE
  HORIZON EUROPE Marie Sklodowska-Curie Actions

  Award: 101073037