Forthcoming special issue

Special Issue on the Application of Machine Learning and Artificial Intelligence in Fault Diagnostics

The rapid development of data science and associated artificial intelligence (AI) methods has seen a substantial increase in interest in their application to anomaly detection, fault diagnostic and prognostic challenges across a wide range of industrial and civil applications.  

Such approaches may well be the complement that is sought for conventional physical model-based and statistical approaches which often struggle to achieve the desired performance when dealing with complex engineering systems.  

Researchers have started to apply a range of machine learning and AI-based methods to the large scale, multi-dimensional data that is often associated with large scale sensor systems, particularly those which involve IoT devices.  

There is clear scope for the further development of such approaches, such as deep learning, transfer learning methods and AI models, to enhance the performance of condition monitoring and associated technologies, and this is the key motivation for this Special Issue. 

This Special Issue will focus on (but not be limited to) the application of machine learning and AI technologies to the following challenges: 

  • Anomaly detection 
  • Fault diagnostics 
  • Prognostics and remaining useful life prediction 
  • Enhancing predictive maintenance systems 
  • Improving system reliability

Submission of Papers 

Authors are asked to follow the guidelines in “Information for Authors” on the JDMD web page:  

Please submit manuscripts in electronic form through the Manuscript Central web site:  

Please ensure that all submissions are declared as ‘Special Issue on the Application of Machine Learning and Artificial Intelligence to Fault Diagnostics’ on the popup menu for manuscript type. 

  • The deadline for manuscript submissions is April 15th, 2022
  • The publication date for the Special Issue is intended to be July 2022 

Guest Editors for the Special Issue 

  • Prof. Konstantinos Gryllias , KU Leuven, Belgium, konstantinos
  • Dr. Tianyang Wang Tsinghua University, China
  • Dr. Haidong Shao, Hunan University, China