Dual-rotor misalignment fault quantitative identification based on DBN and improved D-S evidence theory

• Main Authors: Dalian Yang, Fanyu Zhang, Jingjing Miao, Hongxian Zhang, Renjie Li, Jie Tao
• Format: Article
• Language: English
• Published: EDP Sciences 2021-01-01
• Series: Mechanics & Industry
• Subjects: deep belief network; mutual information measure; d-s evidence theory; dual-rotor system; misalignment fault quantitative identification
• Online Access: https://www.mechanics-industry.org/articles/meca/full_html/2021/01/mi200199/mi200199.html
• ISSN: 2257-7777; 2257-7750

Misalignment fault is the main factor that affects the normal running of dual-rotor system. Quantitative identification the misalignment fault is an important way to ensure the safe and stable service of the dual-rotor system, while the identification accuracy of traditional methods is low. Aiming at the above problems, this paper proposed a dual-rotor misalignment fault quantitative identification method based on DBN and D-S evidence theory improved by mutual information measure (MIMD-S). Seven groups experiments were conducted and several vibration signals were collected. By comparing it with the traditional methods D-S, and Pignistic improved D-S (PD-S) evidence theory, the results show that the method proposed in this paper improves the accuracy of the misalignment fault quantitative identification of the dual-rotor, the identification error rate was only 0.36%.