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

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.