The Fault Diagnosis of Rolling Bearing Based on Variational Mode Decomposition and Iterative Random Forest. (12th February 2020)

• Record Type: Journal Article
• Authors: Qin, Xiwen; Guo, Jiajing; Dong, Xiaogang; Guo, Yu
• Other Names: Concli Franco Guest Editor.
• Is Part Of: Shock and vibration. Volume 2020(2020)
• Publication Date: 2020-02-12
• Subjects: Shock (Mechanics) -- Periodicals; Vibration -- Periodicals; 534.5
• DOI: 10.1155/2020/1576150
• Languages: English
• ISSNs: 1070-9622

Abstract

Abstract : Rolling bearing is a critical part of machinery, whose failure will lead to considerable losses and disastrous consequences. Aiming at the research of rotating mechanical bearing data, a fault identification method based on Variational Mode Decomposition (VMD) and Iterative Random Forest (iRF) classifier is proposed. Furthermore, EMD and EEMD are used to decompose the data. At the same time, three mainstream classifiers were selected as the benchmark model. The results show that the proposed model has the highest recognition accuracy.