Application analysis of machine learning in fault diagnosis: A bibliometric analysis. (September 2020)

• Record Type: Journal Article
• Authors: Tu, Wenyan; Fang, Ji; He, Yihua; Huang, Jin
• Is Part Of: Journal of physics. Volume 1629(2020)
• Publication Date: 2020-09
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1629/1/012020
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: This article to analyze the application of machine learning in fault diagnosis by using bibliometrics, co-citation network analysis and cluster analysis methods. The analysis found that in the application research of machine learning in fault diagnosis, China has the largest number of published papers and cited papers, among which IEEE access is the most published journal, MECHANICAL SYSTEMS AND SIGNAL PROCESSING is the most cited journal, followed by the United States and India. Using document co-citation relationship analysis, a set of key documents in this field was identified., Currently, the important algorithms fault diagnosis include support vector data description method, transfer learning algorithm, convolutional neural network algorithm, natural inspired optimization algorithm, bayesian network, wavelet packet decomposition algorithm, fuzzy logic algorithm and so on. All of them are clustered according to keywords, and their application fields include acoustic emission and fault diagnosis of doubly-fed induction generators.