Artificial intelligence for fault diagnosis of rotating machinery: A review
Ruonan Liua b, Boyuan Yanga b, Enrico Zioc d, Xuefeng Chena b✉
- aSchool of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- bState Key Laboratory for Manufacturing Systems Engineering, Xian Jiaotong University, Xi’an 710049, China
- cChair on System Science and the Energetic Challenge, EDF Foundation, Laboratoire Genie Industriel, CentraleSupélec, Université Paris-Saclay Grande voie des Vignes, 92290 Chatenay-Malabry, France
- dEnergy Departement, Politecnico di Milano, Milano, Italy
- ✉Corresponding author
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Abstract
Abstract 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.
TL;DRThis 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. (Semantic Scholar)