12 7月 2022 gabriels





Darren Williams,英国焊接学会
Miguel Martínez García,英国拉夫堡大学


Crucial components fault diagnosis has become an indispensable technology in modern industrial complex systems due to the rapid development of high-speed heavy load and complex mechanical equipment. Usually, the condition monitoring tasks are submitted in a sequence during addressing a series of fault diagnosis tasks with increments of working conditions, fault types or machines that often occur in real-world scenarios. Compared with transfer learning- and meta-learning-based fault diagnosis models that focus only on the performance of the model on the target task and perform poorly on previous tasks due to catastrophic forgetting, continual learning-based fault diagnosis model requires good performance on all learned tasks and does not need all historical fault data to retrain the model. Continual learning-based fault diagnosis models can constantly learn knowledge of new fault diagnosis tasks to reduce training costs and accumulate this diagnosis knowledge to improve the reliability and generalization capabilities of the diagnosis model.

To promote effective intelligent condition monitoring, a focused session in this area will be organized as a platform to present high-quality original research on the latest developments of continual learning based condition monitoring methods. Potential topics include but are not limited to the following:

  • Continual learning of deep models for crucial components fault diagnosis and prognosis
  • Degradation analysis for crucial components
  • Cross-domain learning for robust condition monitoring
  • Continual domain adaptation or domain-incremental learning for condition monitoring
  • Condition monitoring with fault types increments
  • Condition monitoring with machine increments
  • Few-shot continual learning for condition monitoring
  • Domain generalization to unseen working conditions of machines
  • Adaptive fault diagnosis model for varying conditions
  • Life-long learning of machine fault diagnosis model




作者可登入期刊主页进行在线投稿,在“文章类型”中选择“特刊文章”,并在“选择特刊”的下拉框中选择“Continuous learning based condition monitoring for crucial components”。



Measurement Science and Technology

● 2021年影响因子:2.398

Measurement Science and Technology(MST)涵盖整个测量科学和传感器技术的理论、实践和应用,包括:精密测量和计量学;传感器和传感器系统;光学和激光技术;流体;成像;光谱学;材料和材料加工;生物、医学和生命科学;环境和大气;新型仪器系统和组件。MST还出版专题综述和特刊。