Artificial intelligence for fault diagnosis of rotating machinery: A review

• Published in: Mechanical systems and signal processing Vol. 108; pp. 33 - 47
• Main Authors: Liu, Ruonan, Yang, Boyuan, Zio, Enrico, Chen, Xuefeng
• Format: Journal Article
• Language: English
• Published: Berlin Elsevier Ltd 01.08.2018; Elsevier BV; Elsevier
• Subjects: Algorithms; Artificial intelligence; Artificial neural network; Artificial neural networks; Bayesian analysis; Deep learning; Fault diagnosis; Industrial applications; k-Nearest neighbour; Machine learning; Naive Bayes; Rotating machinery; Rotation; Statistics; Studies; Support vector machine; Support vector machines; System effectiveness; Fault diagnosis; Deep learning; Naive Bayes; Rotating machinery; Support vector machine; Artificial neural network; Artificial intelligence; k-Nearest neighbour
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2018.02.016

•Surveys on recent applications of artificial intelligence techniques to rotating machinery fault diagnosis.•Provides a guidance of how to choose and use artificial intelligence techniques in engineering.•Describes the artificial intelligence techniques applications and rotating machinery fault diagnosis trends. Fault diagnosis of rotating machinery plays a significant role for the reliability and safety of modern industrial systems. As an emerging field in industrial applications and an effective solution for fault recognition, artificial intelligence (AI) techniques have been receiving increasing attention from academia and industry. However, great challenges are met by the AI methods under the different real operating conditions. This paper attempts to present a comprehensive review of AI algorithms in rotating machinery fault diagnosis, from both the views of theory background and industrial applications. A brief introduction of different AI algorithms is presented first, including the following methods: k-nearest neighbour, naive Bayes, support vector machine, artificial neural network and deep learning. Then, a broad literature survey of these AI algorithms in industrial applications is given. Finally, the advantages, limitations, practical implications of different AI algorithms, as well as some new research trends, are discussed.