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

Authors

DOI:

https://doi.org/10.37965/jdmd.2022.132

Abstract

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 on the Application of Machine Learning and Artificial Intelligence in Fault Diagnostics contains 6 papers.

Author Biography

Tianyang Wang, Tsinghua Univeristy, China

Mainly engaged in research work in the field of fault diagnosis and signal analysis of large rotating machinery such as wind turbines and aero engines. Research directions: Modern signal processing, Dynamic modeling, Artificial intelligence, Pattern recognition.

 

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Published

2022-09-09

How to Cite

Gryllias, K. ., Wang, T., & Shao, H. (2022). Special Issue on the Application of Machine Learning and Artificial Intelligence in Fault Diagnostics. Journal of Dynamics, Monitoring and Diagnostics, 1(3), 125–126. https://doi.org/10.37965/jdmd.2022.132

Issue

Section

Editorial